Abstract:Abstract: The advancement of digital technology has made it easier to create, process, and distribute files—using 317 files from the dataset https://www.kaggle.com/datasets/axon data/selfie-and-official-id-photo-dataset-18k…
t-18k images?select=metadata_image.csv has also introduced new challenges, such as the increasing practice of digital file manipulation that is difficult to detect visually. Therefore, an intelligent digital forensics system that can automatically and accurately detect file authenticity is required. This study aims to develop an intelligent digital forensics system for detecting file manipulation by leveraging metadata analysis and the Random Forest classification method. The methods used include extracting metadata from digital files—such as time information, device details, and processing history—followed by analysis to identify patterns of inconsistency that indicate manipulation. This data is then used as features in the classification process using the Random Forest algorithm to distinguish between original and manipulated files. The results of this study are expected to show that the use of metadata analysis combined with the Random Forest algorithm can improve accuracy in detecting digital file manipulation compared to conventional methods. The resulting system is expected to provide an effective, efficient, and integrated solution to support digital forensic investigations, Based on the test results, the system demonstrated good performance with an accuracy rate of 94%.
Keywords: Digital Forensics;File Manipulation;Metadata Analysis;Random Forest;Classification;Machine Learning
Abstrak:Perkembangan teknologi digital telah meningkatkan kemudahan dalam pembuatan, pengolahan,dan distribusi file sebanyak 317 file, sumber datasets https:// www.kaggle.com/datasets/axondata/selfie-and-official-id-photo-dataset-18k-images?select =metadata_image.csv, namun juga menimbulkan tantangan baru berupa meningkatnya praktik manipulasi file digital yang sulit dideteksi secara kasat mata. Oleh karena itu, diperlukan suatu sistem forensik digital yang cerdas dan mampu mendeteksi keaslian file secara otomatis dan akurat. Penelitian ini bertujuan untuk mengembangkan sistem forensik digital cerdas untuk deteksi manipulasi file dengan memanfaatkan analisis metadata dan metode klasifikasi Random Forest. Metode yang digunakan meliputi proses ekstraksi metadata dari file digital, seperti informasi waktu, perangkat, dan riwayat pengolahan, kemudian dilakukan analisis untuk menemukan pola ketidaksesuaian yang mengindikasikan adanya manipulasi. Selanjutnya, data tersebut digunakan sebagai fitur dalam proses klasifikasi menggunakan algoritma Random Forest untuk membedakan antara file asli dan file yang telah dimanipulasi. Hasil dari penelitian ini diharapkan menunjukkan bahwa penggunaan analisis metadata yang dikombinasikan dengan algoritma Random Forest mampu meningkatkan akurasi dalam mendeteksi manipulasi file digital dibandingkan metode konvensional. Sistem yang dihasilkan dapat memberikan solusi yang efektif, efisien, dan terintegrasi dalam mendukung proses investigasi forensik digital, Berdasarkan hasil pengujian, sistem menunjukkan performa yang baik dengan tingkat akurasi sebesar 94%.
Kata Kunci: Forensik Digital, Manipulasi File, Metadata, Random Forest, Klasifikasi, Machine Learning.
Abstract:Abstract: Manual meeting documentation risks losing important information due to cognitive fatigue. Although automated summarization models have evolved, integrated end-to-end systems for Indonesian spoken language remain…
n highly limited. This study aims to design and evaluate an end-to-end automated meeting summarization architecture that directly integrates Automatic Speech Recognition (ASR) via OpenAI Whisper for transcription and the IndoT5 language model for abstractive summarization. IndoT5 was fine-tuned using a dataset of 486 Indonesian spoken language transcript pairs. Testing was conducted on a CPU infrastructure using MP4, MP3, and WAV formats. Results show the optimal fine-tuning configuration significantly improved accuracy, achieving ROUGE-1 (0.4167), ROUGE-2 (0.1973), and ROUGE-L (0.2701) scores. Computationally, the system achieved a Real-Time Factor below 1, processing data faster than the actual recording duration. Conclusively, integrating Whisper and IndoT5 shows potential in producing coherent meeting summaries with lightweight computational overhead, making it viable for local infrastructure implementation to ensure data privacy.
Keywords: abstractive summarization; ASR; end-to-end pipeline; IndoT5; real-time factor
Abstrak: Dokumentasi rapat manual rentan menghilangkan informasi penting akibat keterbatasan kognitif. Meskipun model peringkas otomatis telah berkembang, implementasi sistem terintegrasi (end-to-end) khusus percakapan lisan berbahasa Indonesia masih sangat terbatas. Penelitian ini bertujuan merancang dan mengevaluasi arsitektur peringkas rapat otomatis end-to-end yang mengintegrasikan langsung Automatic Speech Recognition (ASR) melalui OpenAI Whisper untuk transkripsi dan model bahasa IndoT5 untuk peringkasan abstraktif. Adaptasi domain dilakukan melalui fine-tuning IndoT5 menggunakan 486 pasang dataset transkrip lisan berbahasa Indonesia. Pengujian pada infrastruktur CPU menggunakan format MP4, MP3, dan WAV. Hasil pengujian menunjukkan konfigurasi fine-tuning optimal berhasil meningkatkan akurasi, dengan skor ROUGE-1 (0,4167), ROUGE-2 (0,1973), dan ROUGE-L (0,2701). Sistem mendemonstrasikan efisiensi komputasi dengan nilai Real-Time Factor di bawah 1, mengindikasikan waktu pemrosesan lebih cepat dari durasi rekaman asli. Kesimpulannya, integrasi Whisper dan IndoT5 menunjukkan potensi dalam menghasilkan ringkasan yang koheren dengan beban komputasi ringan, sehingga layak diimplementasikan pada infrastruktur lokal organisasi untuk menjaga privasi data.
Kata kunci: ASR; end-to-end pipeline; IndoT5; peringkasan abstraktif; real-time factor
Abstract:This study aimed to compare the performance of machine learning algorithms and user experience in predicting students’ academic achievement. The research is motivated by the need for prediction systems that are not only…
y highly accurate but also easily interpretable by users. The proposed methodology involved the implementation of two algorithms, namely Decision Tree and Random Forest, using an academic dataset that included grade point average, attendance, and assessment scores. Model performance was evaluated using accuracy, precision, recall, and F1-score, while user experience was assessed through the System Usability Scale (SUS) based on a simple user interface. The findings revealed that Random Forest achieved higher predictive accuracy, whereas Decision Tree provided better interpretability and ease of understanding for users. These results indicated a trade-off between model performance and user experience, suggesting that algorithm selection should consider both aspects in order to develop an effective and user-friendly academic prediction system
Abstract:Abstract: Anomalous sound detection is essential for industrial predictive maintenance, as machine failures often originate from subtle acoustic changes during operation. However, high background noise and limitations of…
conventional Convolutional Neural Networks (CNN) reduce detection reliability. This study proposes a 1D-CNN-based anomaly detection framework with multi-view feature fusion and temporal segmentation to enhance detection performance. The approach combines MFCC, Log-Mel Spectrogram, and Chroma STFT features, while temporal segmentation divides audio signals into 5-second segments to better capture transient anomalies. Experiments on the MIMII dataset under varying Signal-to-Noise Ratio (SNR) conditions show that MFCC and Log-Mel fusion achieves the best performance, with 97.90% accuracy and ROC-AUC of 0.9789. The model maintains accuracy above 90% at −6 dB, demonstrating strong robustness in noisy industrial environments.
Keywords: industrial anomaly detection; 1D-CNN; multi-view feature fusion; temporal segmentation; MIMII dataset.
Abstrak: Deteksi anomali suara merupakan komponen penting dalam sistem pemeliharaan prediktif industri, karena kegagalan mesin sering diawali oleh perubahan akustik yang bersifat halus selama proses operasi. Namun, tingkat kebisingan yang tinggi serta keterbatasan arsitektur Convolutional Neural Network (CNN) konvensional dapat menurunkan keandalan deteksi. Penelitian ini bertujuan mengusulkan kerangka deteksi anomali berbasis 1D-CNN yang mengintegrasikan strategi fusi fitur multi-view dan segmentasi temporal untuk meningkatkan kinerja deteksi. Pendekatan yang digunakan menggabungkan fitur MFCC, Log-Mel Spectrogram dan Chroma STFT, sementara teknik temporal splitting membagi sinyal audio menjadi segmen berdurasi 5 detik untuk menangkap anomali yang bersifat sementara. Eksperimen menggunakan dataset MIMII pada berbagai kondisi Signal-to-Noise Ratio (SNR) menunjukkan bahwa kombinasi MFCC dan Log-Mel Spectrogram menghasilkan kinerja terbaik dengan akurasi 97,90% dan ROC-AUC sebesar 0,9789. Model juga mempertahankan akurasi di atas 90% pada kondisi kebisingan ekstrem (−6 dB) yang menunjukkan ketahanan yang baik dalam lingkungan industri yang bising.
Kata kunci: deteksi anomali industri; 1D-CNN; fusi fitur multi-view; segmentasi temporal; dataset MIMII
Abstract:Abstract: The rapid growth of e-commerce mobile applications has generated large volumes of user reviews, making manual sentiment analysis increasingly impractical. This study aims to compare the effectiveness of three machine…
achine learning algorithms Support Vector Machine (SVM), Random Forest, and Naive Bayes for automated sentiment classification of Indonesian-language mobile application reviews. A dataset of 3,000 user reviews from the RupaRupa application on the Google Play Store was collected and preprocessed through normalization, tokenization, stopword removal, and stemming. TF-IDF vectorization was applied for feature extraction, while the Synthetic Minority Over-sampling Technique (SMOTE) was used to address class imbalance across three sentiment categories: positive, negative, and neutral. The results show that SVM achieved the highest accuracy of 90.02%, while Random Forest obtained the best F1-score of 88.08% when sufficient training data were available. Naive Bayes demonstrated relatively stable performance across varying training data sizes. Furthermore, TF-IDF keyword analysis revealed that negative reviews were primarily associated with delivery issues, technical problems, and pricing concerns. These findings demonstrate the effectiveness of machine learning approaches for sentiment classification and provide practical insights for improving mobile application services.
Keywords: sentiment analysis; machine learning; SMOTE; TF-IDF; text classification
Abstrak: Pertumbuhan pesat aplikasi mobile e-commerce telah menghasilkan volume ulasan pengguna yang sangat besar, sehingga analisis sentimen secara manual menjadi semakin tidak praktis. Penelitian ini bertujuan untuk membandingkan efektivitas tiga algoritma machine learning Support Vector Machine (SVM), Random Forest, dan Naive Bayes dalam melakukan klasifikasi sentimen otomatis terhadap ulasan aplikasi mobile berbahasa Indonesia. Dataset yang digunakan terdiri dari 3.000 ulasan pengguna aplikasi RupaRupa yang dikumpulkan dari Google Play Store. Data kemudian diproses melalui tahapan preprocessing yang meliputi normalisasi, tokenisasi, penghapusan stopword, dan stemming. Ekstraksi fitur dilakukan menggunakan metode Term Frequency–Inverse Document Frequency (TF-IDF), sedangkan ketidakseimbangan kelas ditangani menggunakan Synthetic Minority Over-sampling Technique (SMOTE) pada tiga kategori sentimen, yaitu positif, negatif, dan netral. Hasil penelitian menunjukkan bahwa SVM mencapai tingkat akurasi tertinggi sebesar 90,02%, sementara Random Forest memperoleh nilai F1-score terbaik sebesar 88,08% ketika tersedia data pelatihan yang memadai. Naive Bayes menunjukkan performa yang relatif stabil pada berbagai ukuran data pelatihan. Selain itu, analisis kata kunci berbasis TF-IDF mengungkapkan bahwa ulasan negatif terutama berkaitan dengan masalah pengiriman, kendala teknis aplikasi, dan isu harga. Temuan ini menunjukkan bahwa pendekatan machine learning efektif untuk klasifikasi sentimen serta memberikan wawasan yang bermanfaat dalam meningkatkan kualitas layanan aplikasi mobile.
Kata Kunci: analisis sentimen; pembelajaran mesin; SMOTE; TF-IDF; klasifikasi teks.
Abstract:Abstract: Automated batik motif classification is challenged by high inter-class similarity and texture complexity. This study proposes a hybrid model integrating MobileNetV2 as a feature extractor and Support Vector Machine…
hine (SVM) as the classifier to optimize accuracy and efficiency. Utilizing a Kaggle dataset of 8,640 images across 20 batik categories, the data was partitioned into 420 training images per class (Dayak: 360) and 15 testing images per class. The results demonstrate superior performance with 96.00% accuracy, exceeding the 90% target. The system showed high computational efficiency with a total execution time of 359.92 seconds and feature extraction taking only 22.63 seconds. This hybrid approach provides an ideal performance balance for resource-constrained mobile applications.
Keywords: batik classification; MobileNetV2; support vector machine; hybrid model; computational efficiency
Abstrak: Klasifikasi motif batik secara otomatis menghadapi tantangan kemiripan visual antar-kelas yang tinggi. Penelitian ini bertujuan mengoptimalkan akurasi dan efisiensi pengenalan batik menggunakan model hibrida MobileNetV2 sebagai pengekstraksi fitur dan Support Vector Machine (SVM) sebagai klasifikator. Menggunakan dataset Kaggle berisi 8.640 citra dari 20 kategori batik, data dibagi menjadi 420 citra latih per kelas (kecuali Batik Dayak 360) dan 15 citra uji per kelas. Hasil eksperimen menunjukkan performa impresif dengan akurasi 96,00%, melampaui target awal 90%. Sistem ini sangat efisien dengan total waktu eksekusi 359,92 detik, di mana ekstraksi fitur hanya membutuhkan 22,63 detik. Kombinasi MobileNetV2 dan SVM memberikan keseimbangan performa ideal untuk implementasi pada perangkat bergerak dengan sumber daya terbatas.
Kata kunci: klasifikasi batik; MobileNetV2; Support Vector Machine; Hybrid Model; efisiensi komputasi
Abstract:Abstract: Tech Kios Laptop Kisaran is a business engaged in selling used laptops with various brands and specifications to meet customer needs. However, the selection process is still conducted manually and relies on subjective…
jective judgment, which may result in less accurate recommendations. This study aims to design and implement a Decision Support System using the MOORA (Multi-Objective Optimization on the Basis of Ratio Analysis) method to objectively determine the best used laptop. The criteria applied in this study include brand, screen resolution, laptop size, and battery durability. The system was developed through requirement analysis, system design, implementation, and black-box testing. The results show that the system successfully generates rankings based on MOORA preference values. The highest optimization value of 0.4321 was achieved by Lenovo IdeaPad Slim (A04) and Lenovo ThinkPad (A06), indicating that these two alternatives are the best recommended used laptops. Therefore, the developed system enhances the objectivity, effectiveness, and accuracy of the laptop selection process at Tech Kios Laptop Kisaran.
Keywords: decision support system; MOORA; multi criteria; used laptop; recommendation.
Abstrak: Tech Kios Laptop Kisaran merupakan usaha yang bergerak di bidang penjualan laptop bekas dengan berbagai merek dan spesifikasi untuk memenuhi kebutuhan pelanggan. Namun, proses pemilihan laptop masih dilakukan secara manual dan bergantung pada penilaian subjektif, sehingga berpotensi menghasilkan rekomendasi yang kurang akurat. Penelitian ini bertujuan untuk merancang dan mengimplementasikan Sistem Pendukung Keputusan menggunakan metode MOORA (Multi-Objective Optimization on the Basis of Ratio Analysis) guna menentukan laptop bekas terbaik secara objektif. Kriteria yang digunakan dalam penelitian ini meliputi merek, resolusi layar, ukuran laptop, dan ketahanan daya baterai. Pengembangan sistem dilakukan melalui tahapan analisis kebutuhan, perancangan sistem, implementasi, serta pengujian menggunakan metode black-box. Hasil penelitian menunjukkan bahwa sistem mampu menghasilkan peringkat alternatif berdasarkan nilai preferensi MOORA. Nilai optimasi tertinggi sebesar 0,4321 diperoleh oleh Lenovo IdeaPad Slim (A04) dan Lenovo ThinkPad (A06), yang menunjukkan bahwa kedua alternatif tersebut merupakan rekomendasi laptop bekas terbaik. Dengan demikian, sistem yang dikembangkan mampu meningkatkan objektivitas, efektivitas, dan ketepatan dalam proses pemilihan laptop bekas di Tech Kios Laptop Kisaran.
Kata kunci: laptop bekas; MOORA; multi-kriteria; rekomendasi; sistem pendukung keputusan.
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: 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