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: The rapid growth of e-commerce in Indonesia has increased consumer interactions with digital platforms, particularly Lazada, Tokopedia, and Blibli, resulting in a large volume of customer reviews that reflect consumer…
onsumer experiences and perceptions but have not been optimally utilized in business decision-making. The main issue addressed in this study is how to process customer review data to generate meaningful information regarding consumer opinions. This research aims to apply web scraping techniques to collect customer review data and conduct sentiment analysis to identify trends in consumer opinions across the three e-commerce platforms. The dataset consists of 3,000 customer reviews, with 1,000 reviews collected from each platform, covering aspects such as shopping experience, service quality, delivery process, and customer satisfaction. The research methodology includes data collection through web scraping, text preprocessing for data cleaning and normalization, sentiment analysis using machine learning approaches, and visualization of sentiment results. The findings indicate differences in the distribution of positive, negative, and neutral sentiments across platforms, reflecting variations in consumer experiences and service strategies. These results demonstrate that sentiment analysis based on customer reviews can serve as strategic input to improve service quality, business performance, and marketing strategies in Indonesia’s e-commerce sector.
Keywords: customer reviews; digital services; e-commerce; sentiment analysis; web scarping
Abstrak: Pertumbuhan pesat e-commerce di Indonesia meningkatkan interaksi konsumen dengan platform digital, khususnya Lazada, Tokopedia, dan Blibli, yang menghasilkan ulasan pelanggan dalam jumlah besar sebagai cerminan pengalaman dan persepsi konsumen, namun belum dimanfaatkan secara optimal dalam pengambilan keputusan bisnis. Permasalahan utama penelitian ini adalah bagaimana mengolah data ulasan tersebut agar dapat memberikan informasi yang bermakna mengenai opini konsumen. Penelitian ini bertujuan menerapkan web scraping untuk mengumpulkan data ulasan pelanggan serta melakukan analisis sentimen guna mengidentifikasi tren opini konsumen pada ketiga platform e-commerce tersebut. Data yang digunakan berjumlah 3.000 ulasan pelanggan, dengan masing-masing platform diwakili oleh 1.000 ulasan yang mencakup pengalaman berbelanja, kualitas layanan, proses pengiriman, dan tingkat kepuasan pelanggan. Metode penelitian meliputi pengambilan data menggunakan web scraping, pra-pemrosesan teks untuk pembersihan dan normalisasi data, analisis sentimen dengan pendekatan pembelajaran mesin, serta visualisasi hasil sentimen. Hasil penelitian menunjukkan adanya perbedaan distribusi sentimen positif, negatif, dan netral pada setiap platform, yang mencerminkan variasi pengalaman konsumen dan strategi layanan. Temuan ini menunjukkan bahwa analisis sentimen berbasis ulasan pelanggan dapat menjadi masukan strategis untuk meningkatkan kualitas layanan, kinerja bisnis, dan strategi pemasaran e-commerce di Indonesia.
Kata kunci: customer reviews; digital services;e-commerce;sentiment analysis;web scarping
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: This research is motivated by the problem of building material inventory management at Jaqfar Building Store, which is still done manually and based on subjective estimates. This often results in inaccuracies in…
n determining stock levels, either in the form of overstock or understock, which hinders operational effectiveness. The purpose of this study is to apply the Multiple Linear Regression method to analyze the relationship between incoming stock (X1) and outgoing stock (X2) variables with the ending stock variable (Y) to produce an optimal inventory prediction model. The research methodology used includes collecting historical transaction data for building materials such as cement, ceramics, zinc, plywood, and iron. This web-based prediction system was developed using the PHP programming language and a MySQL database. The analysis results show that the resulting regression model can provide a mathematical picture of future inventory patterns based on historical data. Implementation of this system is expected to assist the management of Jaqfar Building Materials Store in making strategic decisions regarding purchasing and sales in a more measured and efficient manner.
Keyword: building materials; data mining; inventory; multiple linear regression
Abstrak: Penelitian ini dilatarbelakangi oleh permasalahan pengelolaan persediaan bahan bangunan di Toko Bangunan Jaqfar yang masih dilakukan secara manual dan berdasarkan perkiraan subjektif. Hal ini menyebabkan sering terjadinya ketidaktepatan dalam menentukan jumlah stok, baik berupa kelebihan barang (overstock) maupun kekurangan barang (understock) yang menghambat efektivitas operasional. Tujuan dari penelitian ini adalah menerapkan metode Multiple Linear Regression (Regresi Linear Berganda) untuk menganalisis hubungan antara variabel stok masuk (X1) dan stok keluar (X2) terhadap variabel stok akhir (Y) guna menghasilkan model prediksi persediaan yang optimal. Metodologi penelitian yang digunakan mencakup pengumpulan data historis transaksi bahan bangunan seperti semen, keramik, seng, triplek, dan besi. Sistem prediksi ini dikembangkan berbasis web menggunakan bahasa pemrograman PHP dan basis data MySQL. Hasil analisis menunjukkan bahwa model regresi yang dihasilkan mampu memberikan gambaran matematis mengenai pola persediaan di masa mendatang berdasarkan data historis. Implementasi sistem ini diharapkan dapat membantu manajemen Toko Bangunan Jaqfar dalam mengambil keputusan strategis terkait pembelian dan penjualan secara lebih terukur serta efisien.
Kata kunci: bahan bangunan; data mining; persediaan; regresi linear berganda
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: Human metapneumovirus (HMPV) poses a global health threat, but its detection remains challenging due to limited environmental monitoring. This study aims to develop a portable diagnostic tool for rapid HMPV detection…
ection by integrating cutting-edge biotechnology (CRISPR-Cas system and immunoassay) with air quality sensors on an Internet of Things (IoT)-based microfluidic platform controlled by an ESP32 microcontroller. The system is supported by a companion application and data analysis using Vertex AI, and is capable of providing results in less than fifteen minutes. The development results demonstrate the potential for improving detection accuracy and reliability, particularly with further development of virus-specific biosensors, sensor optimization, and algorithms. This technology is effective as a complementary tool for early screening and environment-based risk management in areas with limited laboratory facilities, although it does not completely replace molecular diagnostic methods such as PCR. A rapid diagnostic approach based on environmental sensors, IoT, and artificial intelligence is a promising strategy to improve early HMPV detection, accelerate public health responses, and strengthen respiratory infection prevention through integrated environmental monitoring and education functions.
Keywords: air quality; CRISPR-Cas; Human Metapneumovirus (HMPV); Internet of Things, portable diagnostic; public health; rapid detection; sensors.
Abstrak: Human metapneumovirus (HMPV) merupakan ancaman bagi kesehatan global, namun pendeteksiannya masih sulit akibat keterbatasan pemantauan lingkungan. Studi ini bertujuan mengembangkan alat diagnostik portabel untuk deteksi cepat HMPV melalui integrasi bioteknologi mutakhir (sistem CRISPR-Cas dan immunoassay) dengan sensor kualitas udara pada platform mikrofluida berbasis Internet of Things (IoT) yang dikendalikan mikrokontroler ESP32. Sistem ini didukung aplikasi pendamping dan analisis data menggunakan Vertex AI, serta mampu memberikan hasil dalam waktu kurang dari lima belas menit. Hasil pengembangan menunjukkan potensi peningkatan akurasi dan keandalan deteksi, terutama dengan pengembangan lanjutan berupa biosensor spesifik virus, optimalisasi sensor, dan algoritma. Teknologi ini efektif sebagai alat pelengkap untuk skrining awal dan manajemen risiko berbasis lingkungan di wilayah dengan keterbatasan fasilitas laboratorium, meskipun tidak sepenuhnya menggantikan metode diagnostik molekuler seperti PCR. Pendekatan diagnostik cepat berbasis sensor lingkungan, IoT, dan kecerdasan buatan menjadi strategi menjanjikan untuk meningkatkan deteksi dini HMPV, mempercepat respons kesehatan masyarakat, serta memperkuat pencegahan infeksi saluran pernapasan melalui fungsi pemantauan dan edukasi lingkungan yang terintegrasi.
Kata kunci: CRISPR-Cas; diagnostik portabel; deteksi cepat; Human Metapneumovirus (HMPV); kesehatan masyarakat; IoT (Internet of Things); sensor kualitas udara.
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: The materials sector is one of the stock markets sectors that attracts investors due to the high level of construction activity in Indonesia, which supports long-term growth. Stock price movements are influenced…
d by various factors, requiring investors to determine the appropriate timing for buying, selling, or holding stocks. Therefore, this study aims to predict stock prices in the materials sector using a combination of CNN–BiLSTM algorithms. The research data were obtained from Yahoo Finance and processed through min–max normalization, data splitting, sliding window, model implementation, and evaluation stages. Testing was conducted on INTP and SMGR stocks with data split scenarios ranging from 60:40 to 90:10. The results show that CNN–BiLSTM performs best with a 90:10 data split, with minimum MSE and MAPE values of 0.000153 and 2.471% for INTP, and 0.000199 and 2.208% for SMGR, respectively. These findings indicate that increasing the proportion of training data improves the model's ability to learn historical patterns and produce more stable predictions.
Keywords: CNN-BILSTM; materials sector; stock
Abstrak: Sektor materials merupakan salah satu sektor saham yang diminati investor karena tingginya aktivitas pembangunan di Indonesia yang mendorong pertumbuhan jangka panjang. Pergerakan harga saham dipengaruhi oleh berbagai faktor sehingga investor perlu menentukan waktu transaksi yang tepat. Oleh karena itu, penelitian ini bertujuan memprediksi harga saham sektor materials menggunakan kombinasi algoritma CNN–BiLSTM. Data penelitian diperoleh dari Yahoo Finance dan diproses melalui tahapan normalisasi min–max, pembagian data, sliding window, implementasi model, serta evaluasi. Pengujian dilakukan pada saham INTP dan SMGR dengan skenario pembagian data 60:40 hingga 90:10. Hasil menunjukkan bahwa CNN–BiLSTM menghasilkan performa terbaik pada pembagian data 90:10, dengan nilai MSE dan MAPE minimum masing-masing sebesar 0.000153 dan 2.471% untuk INTP, serta 0.000199 dan 2.208% untuk SMGR. Temuan ini mengindikasikan bahwa peningkatan porsi data latih meningkatkan kemampuan model dalam mempelajari pola historis dan menghasilkan prediksi yang lebih stabil.
Kata kunci: CNN-BILSTM; saham; sektor materials