Abstract:Abstract: The development of information technology encourages retail businesses to manage customer and transaction data more effectively. However, many small-scale retailers still rely on manual record-keeping, resulting…
g in unintegrated data and limited decision-making support. This study aims to design and implement a web-based Electronic Customer Relationship Management (E-CRM) system called MySaffana for Saffana Gallery Boutique to optimize customer and transaction data management. The research method includes requirement analysis, system design using UML, implementation using PHP and MySQL, and system testing using black box testing. The results show that the MySaffana system is able to manage customer data, products, transactions, and sales reports in an integrated and efficient manner. System testing indicates that all main features function properly and meet user requirements. Therefore, the developed E-CRM system provides an effective and practical solution for strengthening data-driven decision-making in small-scale retail businesses.
Keywords: boutique; customer data management; customer profiling; E-CRM.
Abstrak: Perkembangan teknologi informasi mendorong usaha ritel untuk mengelola data pelanggan dan transaksi secara lebih efektif. Namun, banyak usaha ritel skala kecil masih melakukan pencatatan secara manual sehingga data tidak terintegrasi dan kurang optimal dalam mendukung keputusan. Penelitian ini bertujuan untuk merancang dan mengimplementasikan sistem Electronic Customer Relationship Management (E-CRM) berbasis web bernama MySaffana pada Butik Saffana Gallery yang dapat mengoptimalkan pengelolaan data pelanggan dan transaksi. Metode penelitian meliputi analisis kebutuhan, perancangan sistem menggunakan UML, implementasi dengan PHP dan MySQL, serta pengujian menggunakan metode black box testing. Hasil penelitian menunjukkan bahwa sistem MySaffana mampu mengelola data pelanggan, produk, transaksi, dan laporan penjualan secara terintegrasi dan efisien. Pengujian sistem membuktikan seluruh fitur berjalan sesuai fungsi dan kebutuhan pengguna. Dengan demikian, sistem E-CRM berbasis web ini dapat menjadi solusi yang efektif dalam mendukung pengelolaan data dan pengambilan keputusan berbasis data bagi usaha ritel skala kecil.
Kata kunci: butik; E-CRM; profil pelanggan; pengelolaan data pelanggan.
Abstract:Abstract: Rehabilitation programs are essential in correctional systems to equip inmates with the skills and behavioral readiness required for social reintegration. However, rehabilitation program assignment in many correctional…
ectional institutions remains dependent on manual and subjective assessments, which may result in inconsistent decisions. This study develops a Random Forest–based prediction system to support objective and data-driven rehabilitation program determination. A quantitative approach was applied using historical inmate data from January 2023 to January 2025, comprising 2,023 records. The research process included data preprocessing, an 80:20 training–testing split, model training, and performance evaluation using accuracy, precision, recall, and F1-score metrics. The results show that the model achieved an accuracy of 86.17% during training in Google Colab and 68.83% when deployed within the application system. This performance gap reflects real-world deployment and computational constraints rather than model failure. The proposed system provides consistent and objective rehabilitation program recommendations, thereby supporting more effective rehabilitation planning and decision-making in correctional institutions.
Keywords: correctional institutions; inmate rehabilitation programs; machine learning; random Forest; prediction system
Abstrak: Program pembinaan narapidana memiliki peran penting dalam sistem pemasyarakatan untuk membekali warga binaan dengan keterampilan serta kesiapan perilaku dalam proses reintegrasi ke masyarakat. Namun, pada banyak lembaga pemasyarakatan, penentuan program pembinaan masih bergantung pada penilaian manual yang bersifat subjektif, sehingga berpotensi menimbulkan ketidakkonsistenan dalam pengambilan keputusan. Penelitian ini mengembangkan sistem prediksi program pembinaan narapidana berbasis algoritma Random Forest guna mendukung pengambilan keputusan yang objektif dan berbasis data. Pendekatan kuantitatif diterapkan menggunakan data historis narapidana periode Januari 2023 hingga Januari 2025 sebanyak 2.023 data. Tahapan penelitian meliputi prapemrosesan data, pembagian data latih dan uji dengan rasio 80:20, pelatihan model, serta evaluasi performa menggunakan metrik akurasi, precision, recall, dan F1-score. Hasil penelitian menunjukkan bahwa model mencapai akurasi sebesar 86,17% pada tahap pelatihan di Google Colab dan 68,83% saat diimplementasikan pada sistem aplikasi. Perbedaan performa tersebut mencerminkan keterbatasan lingkungan operasional, bukan kegagalan model. Secara keseluruhan, sistem yang dikembangkan mampu memberikan rekomendasi program pembinaan yang lebih objektif dan konsisten, sehingga mendukung perencanaan pembinaan yang lebih efektif.
Kata kunci: mesin pembelajaran; program pembinaan narapidana; random Forest; sistem pemasyarakatan; sistem prediksi
Abstract:Abstract: Inventory management of consumable medical devices and drugs plays a crucial role in maintaining the continuity of healthcare operations. However, Jelita Dental Care still faces challenges in recording and controlling…
rolling stock due to manual procedures, which can lead to data inaccuracy, procurement delays, and the risk of stockouts. To address these issues, this study aims to develop a web-based Electronic Supply Chain Management (E-SCM) system that integrates stock monitoring and procurement processes. The Reorder Point (ROP) method is applied to determine the optimal reorder point based on average demand, lead time, and safety stock. This system was built using the PHP programming language and MySQL database. The results show that the JelitaMed system is able to improve the effectiveness and accuracy of inventory management, simplify the structured procurement submission process between the admin, owner, and supplier, and support decision-making in maintaining the availability of consumable medical devices and drugs. Thus, the implementation of E-SCM combined with the ROP method is a practical solution to improve inventory control in small-scale health clinics.
Keywords: e-scm; inventory; information system; medical supplies; reorder point.
Abstrak: Pengelolaan persediaan alat dan obat medis habis pakai memiliki peran penting dalam menjaga keberlangsungan operasional layanan kesehatan. Namun, Jelita Dental Care masih menghadapi kendala dalam pencatatan dan pengendalian stok akibat prosedur manual, yang dapat menyebabkan ketidaktepatan data, keterlambatan pengadaan, serta risiko kekurangan persediaan. Untuk mengatasi permasalahan tersebut, penelitian ini bertujuan mengembangkan sistem Electronic Supply Chain Management (E-SCM) berbasis web yang mengintegrasikan pemantauan stok dan proses pengadaan. Metode Reorder Point (ROP) diterapkan untuk menentukan waktu pemesanan ulang yang optimal berdasarkan permintaan rata-rata, lead time, dan safety stock. Sistem ini dibangun menggunakan bahasa pemrograman PHP dan database MySQL. Hasil penelitian menunjukkan bahwa sistem JelitaMed mampu meningkatkan efektivitas dan akurasi pengelolaan persediaan, mempermudah proses pengajuan pengadaan secara terstruktur antara admin, owner, dan supplier, serta mendukung pengambilan keputusan dalam menjaga ketersediaan alat dan obat medis habis pakai. Dengan demikian, penerapan E-SCM yang dikombinasikan dengan metode ROP menjadi solusi praktis untuk meningkatkan pengendalian persediaan pada klinik kesehatan skala kecil.
Kata kunci: alat medis; e-scm; persediaan; reorder point; sistem informasi
Abstract:Abstract: Academic achievement mapping is an important process in higher education to support effective academic monitoring and guidance. In practice, student grouping is often conducted manually by academic staff using…
simple criteria such as Grade Point Average (GPA) thresholds and subjective judgment, without systematic data analysis. This study aims to apply the Fuzzy C-Means (FCM) clustering algorithm to objectively group students based on their academic achievement levels. The dataset consists of academic records from 179 sixth-semester students of the Computer Science Study Program at Universitas Islam Negeri Sumatera Utara, where 160 eligible students are processed in the FCM calculation. Three variables are used: cumulative GPA, total completed credits, and the total number of low grades (D/E). The FCM algorithm automatically performs the mapping and groups students into three categories, namely excellent, stable, and at-risk students. Cluster quality is evaluated using the Silhouette Score and Davies–Bouldin Index, showing satisfactory clustering performance. The results indicate that the proposed approach provides a data-driven and objective basis for academic decision support.
Keywords: academic achievement; clustering; fuzzy c-means; student
Abstrak: Pemetaan pencapaian akademik mahasiswa merupakan proses penting dalam pendidikan tinggi untuk mendukung pemantauan dan pembinaan akademik yang tepat sasaran. Dalam praktiknya, pengelompokan mahasiswa masih sering dilakukan secara manual oleh pihak akademik berdasarkan kriteria sederhana, seperti batasan Indeks Prestasi Kumulatif (IPK) dan penilaian subjektif, tanpa analisis data yang sistematis. Penelitian ini bertujuan menerapkan algoritma Fuzzy C-Means (FCM) untuk mengelompokkan mahasiswa secara objektif berdasarkan tingkat pencapaian akademik. Data penelitian berasal dari 179 mahasiswa semester enam Program Studi Ilmu Komputer Universitas Islam Negeri Sumatera Utara, dengan 160 mahasiswa memenuhi kriteria dan diproses menggunakan algoritma FCM. Variabel yang digunakan meliputi IPK kumulatif, jumlah SKS yang telah ditempuh, dan total nilai rendah (D/E). Proses pemetaan sepenuhnya dilakukan oleh algoritma FCM dan menghasilkan tiga kategori mahasiswa, yaitu unggul, stabil, dan berisiko. Evaluasi menggunakan Silhouette Score dan Davies–Bouldin Index menunjukkan kualitas pengelompokan yang cukup baik.
Kata kunci: fuzzy c-means; clustering; mahasiswa; pencapaian akademik
Abstract:Abstract: Distributors in the Fast Moving Consumer Goods (FMCG) sector, such as CV. Sahabat Jaya Sukses, face significant challenges in inventory control, particularly related to product expiration and stock discrepancies…
s caused by manual recording. This study aims to design and implement a web-based Supply Chain Management (SCM) system that integrates the flow of goods from suppliers to retailers by applying the First Expired First Out (FEFO) method to minimize financial losses due to expired products. The research methodology employs the Waterfall model, which is selected because of its structured and systematic development stages and its suitability for systems with clear and stable requirements, facilitating effective analysis, design, implementation, and testing processes. The research stages include requirements analysis, system design, implementation, and testing. The results show that the SCM system successfully integrates data across the entire supply chain, automates inventory recording, and effectively prioritizes product distribution based on the nearest expiration dates. Black Box testing confirms that all system functionalities, including FEFO logic, operate properly, thereby improving operational efficiency and data accuracy.
Keywords: supply chain management; FEFO; web-based system; distributor; inventory
controls
Abstrak: Distributor di sektor Fast Moving Consumer Goods (FMCG) seperti CV. Sahabat Jaya Sukses menghadapi tantangan dalam pengendalian persediaan, khususnya terkait produk kedaluwarsa dan selisih stok akibat pencatatan manual. Penelitian ini bertujuan merancang dan mengimplementasikan sistem Supply Chain Management (SCM) berbasis web yang mengintegrasikan aliran barang dari pemasok hingga pengecer dengan menerapkan metode First Expired First Out (FEFO) untuk meminimalkan kerugian akibat produk kedaluwarsa. Metodologi penelitian menggunakan model Waterfall yang dipilih karena memiliki tahapan pengembangan yang terstruktur, sistematis, dan sesuai dengan kebutuhan sistem yang jelas serta stabil, sehingga memudahkan proses perancangan, implementasi, dan pengujian. Tahapan penelitian meliputi analisis kebutuhan, desain sistem, implementasi, dan pengujian. Hasil penelitian menunjukkan bahwa sistem SCM berhasil mengintegrasikan data di seluruh rantai pasok, mengotomatisasi pencatatan stok, serta memprioritaskan distribusi barang berdasarkan tanggal kedaluwarsa terdekat. Pengujian Black Box membuktikan bahwa seluruh fungsi sistem, termasuk logika FEFO, berjalan dengan baik sehingga meningkatkan efisiensi operasional dan akurasi data.
Kata kunci: distributor; FEFO; manajemen rantai pasok; pengendalian stok; sistem berbasis web
Abstract:Abstract: The growing intensity of cyber attacks, marked by rapid, large-scale, automated, and adaptive execution, requires analytical methods that represent the diversity of network environments, including variations in…
target platforms such as IoT, traditional networks, and hybrid infrastructures. This study compares machine learning models for cyber attack classification under heterogeneous environmental conditions and formulates a conceptual optimization framework based on model performance. Four publicly available benchmark datasets were used, namely UNB CIC IoT 2023, UNB CIC IDS-2018, UNSW-NB15, and a Kaggle cyber security attacks dataset, comprising approximately 40,000 to over 3.6 million records and 25 to 80 features across IoT, conventional, and mixed network environments. Random Forest, XGBoost, Multilayer Perceptron, and Transformer were implemented within a unified pipeline involving preprocessing, feature selection, and Bayesian Optimization-based hyperparameter tuning. All models achieved F1-score and Cohen's Kappa above 96%, with XGBoost performing best (97.80%, 97.26%), followed by Random Forest (97.78%, 96.96%) and Transformer (97.44%, 96.82%), while MLP scored lowest (96.74%, 96.00%), a gap below one percentage point. Confusion matrix analysis revealed persistent misclassification in minority and overlapping attack classes, informing a proposed adaptive cyber attack simulation optimization framework.
Keywords: cyber attacks; optimization; machine learning; environmental variability.
Abstrak: Meningkatnya intensitas serangan siber yang berlangsung cepat, masif, otomatis, dan adaptif menuntut pendekatan analitis yang merepresentasikan keragaman lingkungan jaringan, termasuk perbedaan karakteristik platform sasaran seperti Internet of Things (IoT), jaringan konvensional, dan infrastruktur hibrida. Penelitian ini membandingkan model machine learning untuk klasifikasi serangan siber pada kondisi lingkungan heterogen, sekaligus menyusun kerangka optimasi konseptual berdasarkan performa model. Empat dataset benchmark publik digunakan, yaitu UNB CIC IoT 2023, UNB CIC IDS-2018, UNSW-NB15, serta dataset Kaggle cyber security attacks, dengan jumlah data berkisar 40.000 hingga lebih dari 3,6 juta rekaman dan 25 sampai 80 fitur, mewakili lingkungan IoT, konvensional, dan campuran. Random Forest, XGBoost, Multilayer Perceptron, dan Transformer diimplementasikan melalui pipeline terpadu mencakup pra-pemrosesan, seleksi fitur, dan optimasi hyperparameter berbasis Bayesian Optimization. Seluruh model mencapai F1-score dan Cohen's Kappa di atas 96%, dengan XGBoost menunjukkan performa terbaik (97,80%, 97,26%), diikuti Random Forest (97,78%, 96,96%) dan Transformer (97,44%, 96,82%), sementara MLP mencatat skor terendah (96,74%, 96,00%), dengan selisih kurang dari satu poin persentase. Analisis confusion matrix mengungkap misklasifikasi yang konsisten pada kelas minoritas dan serangan dengan karakteristik serupa, yang menjadi dasar kerangka optimasi simulasi serangan siber adaptif yang diusulkan.
Kata kunci: serangan siber; optimasi; machine learning; variabilitas lingkungan
Abstract:Abstract: The rapid development of digital technology has brought significant changes in consumption patterns and customer behavior, particularly in the fashion retail sector. Increasing competition and rising cynsumer expectations…
xpectations for fast, accurate, and technology-based services require businesses to innovate and undergo digital transformation. One widely used approach is the implementation of automation-based Electronic Customer Relationship Management (E-CRM). This study aims to analyze the implementation of E-CRM automation and its impact on service efficiency at Toko Diah Fashion, a fashion retail business that still faces service challenges due to manual systems, such as customer data duplication, delayed responses, and difficulty in monitoring transaction history. The research method used is a descriptive qualitative method with data collection techniques through observation, interviews, and documentation. The research focus is limited to aspects of service efficiency, including service speed, accuracy in managing customer data, and ease in customer follow-up. The E-CRM automation system studied is designed using PHP programming language and a MySQL database. The research results indicate that the implementation of E-CRM automation can significantly improve service efficiency. This system facilitates integrated customer data management, speeds up the service process, and supports more personalized communication through notification features and transaction history recording.
Keyword: automation; e-crm; fashion retail; service efficiency.
Abstrak: Perkembangan teknologi digital yang semakin pesat telah membawa perubahan signifikan dalam pola konsumsi dan perilaku pelanggan, khususnya dalam sektor ritel fashion. Persaingan yang semakin ketat serta meningkatnya ekspektasi konsumen terhadap layanan yang cepat, akurat dan berbasis teknologi menuntut pelaku usaha untuk melakukan inovasi dan transformasi digital. Salah satu pendekatan yang banyak digunakan adalah penerapan Electronic Customer Relationship Management (E-CRM) berbasis automasi. Penelitian ini bertujuan untuk menganalisis penerapan automasi E-CRM serta pengaruhnya terhadap efisiensi pelayanan pada Toko Diah Fashion, sebuah usaha ritel fashion yang masih menghadapi kendala pelayanan akibat sistem manual, seperti duplikasi data pelanggan, keterlambatan respons, dan kesulitan dalam pemantauan histori transaksi. Metode penelitian yang digunakan adalah metode kualitatif deskriptif dengan teknik pengumpulan data melalui observasi, wawancara, dan dokumentasi. Fokus penelitian dibatasi pada aspek efisiensi pelayanan, meliputi kecepatan pelayanan, ketepatan pengelolaan data pelanggan, serta kemudahan dalam tindak lanjut pelanggan. Sistem automasi E-CRM yang dikaji dirancang menggunakan bahasa pemrograman PHP dan basis data MySQL. Hasil penelitian menunjukkan bahwa penerapan automasi E-CRM mampu meningkatkan efisiensi pelayanan secara signifikan. Sistem ini mempermudah pengelolaan data pelanggan secara terintegrasi, mempercepat proses pelayanan, serta mendukung komunikasi yang lebih personal melalui fitur notifikasi dan pencatatan histori transaksi.
Kata kunci: automasi; e-crm; efisiensi pelayanan; ritel fashion.
Abstract:Abstract: Stunting is a chronic nutritional condition in toddlers characterized by a Height-for-Age (HFA) measurement below the standard growth threshold, necessitating early detection to prevent long-term consequences.…
This study aims to classify toddler stunting status by comparing three machine learning methods: Random Forest (RF), K-Nearest Neighbor (KNN), and Support Vector Machine (SVM). The dataset comprises 345 toddler records from Puskesmas Indramayu (2025), including weight, height, and nutritional status based on WFA, HFA, and WFH indicators. Preprocessing steps include data cleaning, StandardScaler normalization, One-Hot Encoding for categorical features, and splitting the training and testing data with a ratio of 80:20. The comparison results are that KNN achieved the best performance with an accuracy of 71.01%, a precision of 0.69, a recall of 0.69, and an F1 score of 0.67, while RF and SVM both had an accuracy of 69.57% with F1 scores of 0.67 and 0.68, respectively. Thus, KNN demonstrated superior effectiveness in classifying the stunting status of toddlers compared to RF and SVM on this dataset.
Keywords: KNN; Random Forest; SVM; Stunting; toddlers
Abstract: Stunting adalah kondisi gizi kronis pada balita yang ditandai dengan pengukuran Tinggi Badan menurut Usia (HFA) di bawah ambang batas pertumbuhan standar, sehingga memerlukan deteksi dini untuk mencegah konsekuensi jangka panjang. Penelitian ini bertujuan untuk mengklasfikasikan status stunting pada balita dengan membandingkan tiga metode pembelajaran mesin: Random Forest (RF), K-Nearest Neighbor (KNN), dan Support Vector Machine (SVM). Kumpulan data terdiri dari 345 catatan balita dari puskesmas indramayu (2025), termaksut brat badan, tinggi badan, dan status gizi berdasarkan indicator WFA, HFA, dan WFH. Langkah-langkah prapemrosesan meliputi pembersian data, normalisasi Stand-ardScaler, One-Hot Encoding untuk fitur kategirikal, serta pembagian data pelatihan dan pengujian dengan rasio 80:20. Hasil perbadingan adalah KNN mencapai kinerja terbaik dengan akurasi 71,01%, presisi 0,69, recall 0,69, dan skor F1 sebesar 0,67, RF dan SVM keduanya memiliki akurasi 69,57% dengan skor F1 masing-masing sebesar 0,67 dan 0,68. Dengan demikian, KNN menunjukkan keefektifan yang lebih unggul dalam mengklasifikasikan status stunting balita dibandingkan dengan RF dan SVM pada da-taset ini.
Kata kunci: KNN; random forest; SVM; Stunting; Balita
Abstract:Abstract: Accurate bone age estimation is essential for monitoring pediatric growth, diagnosing endocrine disorders, and supporting clinical decision-making. Although deep learning has improved prediction accuracy, limited…
ed studies have systematically examined how increasing model depth affects performance and reliability. This study evaluates the effectiveness of progressively deeper convolutional neural networks, specifically EfficientNet variants B0 to B5, for bone age estimation from hand radiographs. Experiments were conducted using 12,611 hand X-ray images from the RSNA Pediatric Bone Age Challenge dataset on Kaggle. To ensure fair comparison, all models were trained using a unified and consistent training pipeline. Model performance was evaluated using Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), Concordance Correlation Coefficient (CCC), and Pearson correlation coefficient. The results show a consistent improvement in prediction accuracy as model depth increases. Among the evaluated models, EfficientNet-B5 achieved the best performance, with an MAE of 21.5 months, MAPE of 6.23%, CCC of 0.9148, and Pearson’s r of 0.9203. These findings confirm that model scaling plays a critical role in enhancing prediction robustness and clinical reliability. Future work should emphasize external validation across diverse populations and incorporate interpretability techniques, such as Grad-CAM, to improve clinical transparency and trust.
Keywords: bone age prediction; deep learning; model evaluation; clinical validation
Abstrak: Estimasi usia tulang yang akurat sangat penting untuk memantau pertumbuhan anak, mendiagnosis gangguan endokrin, dan mendukung pengambilan keputusan klinis. Meskipun pembelajaran mendalam telah meningkatkan akurasi prediksi, studi yang secara sistematis meneliti bagaimana peningkatan kedalaman model memengaruhi kinerja dan keandalan masih terbatas. Studi ini mengevaluasi efektivitas jaringan saraf konvolusional yang semakin dalam, khususnya varian EfficientNet B0 hingga B5, untuk estimasi usia tulang dari radiografi tangan. Eksperimen dilakukan menggunakan 12.611 gambar sinar-X tangan dari dataset RSNA Pediatric Bone Age Challenge di Kaggle. Untuk memastikan perbandingan yang adil, semua model dilatih menggunakan alur pelatihan yang terpadu dan konsisten. Kinerja model dievaluasi menggunakan Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), Concordance Correlation Coefficient (CCC), dan koefisien korelasi Pearson. Hasil menunjukkan peningkatan yang konsisten dalam akurasi prediksi seiring dengan peningkatan kedalaman model. Di antara model yang dievaluasi, EfficientNet-B5 mencapai kinerja terbaik, dengan MAE sebesar 21,5 bulan, MAPE sebesar 6,23%, CCC sebesar 0,9148, dan Pearson’s r sebesar 0,9203. Temuan ini menegaskan bahwa penskalaan model memainkan peran penting dalam meningkatkan optimasi prediksi dan keandalan klinis. Penelitian selanjutnya dapat menekankan validasi eksternal di berbagai populasi dan menggabungkan teknik interpretasi, seperti Grad-CAM, untuk meningkatkan transparansi dan kepercayaan klinis.
Kata kunci: prediksi usia tulang; deep learning; evaluasi model; validasi klinis
Abstract:Abstract: Dazry Harapan Household Industry (IRT) is an SME in Jambi City specializing in the production of laundry perfume and previously relied on manual record-keeping using notebooks. This conventional method created…
several issues, including frequent stock recording errors, difficulties in preparing financial reports, delays in identifying minimum stock levels, and the absence of structured historical data. This study aims to develop a web-based digital recording system to improve efficiency, accuracy, and transparency in inventory management. The system was developed using the Agile (Scrum) methodology through three sprints covering the creation of login modules, stock and transaction recording, reporting, minimum-stock notifications, and interface refinement. System design was formulated using use case diagrams, flowcharts, database modeling, and interface prototypes based on the Laravel framework. User Acceptance Testing (UAT) demonstrated high user satisfaction, with 90% of respondents stating that the system is easy to use, 85% reporting faster administrative processes, and 95% acknowledging improved reporting accuracy. The system also increased recording efficiency by 66%—from 3 minutes to 1 minute per transaction—and reduced stock recording errors from 15% to 2% per month. The results indicate that implementing a web-based digital recording system significantly enhances the operational performance of SMEs.
Keywords: SMEs, digital recording system, Agile, inventory management, Laravel.
Abstrak: Industri Rumah Tangga (IRT) Dazry Harapan merupakan UMKM di Kota Jambi yang bergerak pada produksi parfum laundry dan masih menggunakan sistem pencatatan manual berbasis buku tulis. Metode konvensional tersebut menimbulkan berbagai permasalahan, seperti tingginya kesalahan pencatatan stok, hambatan dalam penyusunan laporan keuangan, keterlambatan identifikasi stok minimum, serta ketiadaan rekap data historis yang terstruktur. Penelitian ini bertujuan mengembangkan sistem pencatatan digital berbasis web untuk meningkatkan efisiensi, akurasi, dan transparansi manajemen persediaan. Pengembangan dilakukan menggunakan metode Agile (Scrum) melalui tiga sprint yang mencakup pembangunan modul login, pencatatan stok, transaksi, laporan, notifikasi stok minimum, serta penyempurnaan antarmuka. Desain sistem dirumuskan menggunakan use case diagram, flowchart, perancangan database, dan prototipe antarmuka berbasis Laravel. Hasil pengujian melalui User Acceptance Test (UAT) menunjukkan tingkat penerimaan pengguna yang tinggi, yaitu 90% menilai sistem mudah digunakan, 85% merasakan percepatan proses pencatatan, dan 95% menilai laporan yang dihasilkan lebih akurat. Efisiensi waktu pencatatan meningkat sebesar 66%, dari 3 menit menjadi 1 menit per transaksi, sedangkan tingkat kesalahan pencatatan menurun dari 15% menjadi 2% per bulan. Penelitian ini membuktikan bahwa implementasi sistem pencatatan digital berbasis web mampu meningkatkan kualitas operasional UMKM secara signifikan.
Kata kunci: UMKM; sistem pencatatan digital; Agile; manajemen stok; Laravel;