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Malcom: Indonesian Journal of Machine Learning and Computer Science
Published by Institut Riset dan Publikasi Indonesia
ISSN : 27972313     EISSN : 27758575     DOI : -
Core Subject : Science,
Computer Science & IT
MALCOM: Indonesian Journal of Machine Learning and Computer Science is a scientific journal published by the Institut Riset dan Publikasi Indonesia (IRPI) in collaboration with several Universities throughout Riau and Indonesia. MALCOM will be published 2 (two) times a year, April and October, each edition containing 10 (Ten) articles. Articles may be written in Indonesian or English. articles are original research results with a maximum plagiarism of 15%. Articles submitted to MALCOM will be reviewed by at least 2 (two) reviewers. The submitted article must meet the assessment criteria and in accordance with the instructions and templates provided by MALCOM. The author should upload the Statement of Intellectual/ Copyright Rights when submitting the manuscript. Papers must be submitted via the Open Journal System (OJS) in .doc or .docx format. The entire process until MALCOM is published will be free of charge. MALCOM is registered in National Library with Number International Standard Serial Number (ISSN) Printed: 2797-2313 and Online 2775-8575. Focus and scope of MALCOM includes Data Mining, Data Science, Artificial Intelligence, Computational Intelligence, Natural Language Processing, Big Data Analytic, Computer Vision, Expert System, Text and Web Mining, Parallel Processing, Intelligence System, Decision Support System and Software Engineering
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 581 Documents
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Pengembangan Sistem Klasifikasi Sentimen Berita PTPN IV Regional III Berbasis Web Menggunakan IndoBERT: Development of a Web-Based News Sentiment Classification System for PTPN IV Regional III Using IndoBERT Wahyudi, Diki; Insani, Fitri; Jasril, Jasril; Yusra, Yusra
MALCOM: Indonesian Journal of Machine Learning and Computer Science Vol. 6 No. 3 (2026): MALCOM July 2026
Publisher : Institut Riset dan Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/malcom.v6i3.2825

Abstract

Aktivitas pemantauan berita di Humas PTPN IV Regional III masih dilakukan secara manual sehingga memerlukan waktu yang lama, rentan terhadap subjektivitas, dan berisiko melewatkan informasi penting. Penelitian ini bertujuan untuk merancang dan membangun sistem pemantauan berita berbasis web yang mengintegrasikan model Indonesian Bidirectional Encoder Representations from Transformers (IndoBERT) untuk klasifikasi sentimen secara otomatis. IndoBERT dipilih karena dilatih khusus menggunakan korpus berbahasa Indonesia sehingga lebih mampu memahami karakteristik bahasa dan sentimen dibandingkan model multilingual. Dataset yang digunakan terdiri atas 1.200 berita dengan distribusi seimbang pada tiga kelas sentimen: positif, netral, dan negatif. Model dikembangkan menggunakan metode fine-tuning dan menghasilkan akurasi 75% serta macro F1-score 0,74. Kelas negatif memperoleh kinerja terbaik dengan F1-score 0,85, sedangkan kelas netral mencapai 0,63 akibat karakteristik sentimen yang ambigu. Sistem ini dikembangkan menggunakan Flask, Next.js, MySQL, dan SerpAPI. Hasil pengujian Black Box menunjukkan bahwa seluruh fitur berfungsi sesuai kebutuhan, sedangkan User Acceptance Testing (UAT) memperoleh tingkat penerimaan sebesar 96,5%. Penelitian ini berkontribusi melalui implementasi sistem pemantauan berita korporasi berbasis web yang mengintegrasikan IndoBERT secara end-to-end.
Sistem Pendeteksi Kendaraan Roda Dua Berbasis Internet of Things Menggunakan ESP32-Cam dan YOLOv3: Internet of Things-Based Two-Wheeled Vehicle Detection System Using ESP32-CAM and YOLOV3 for Smart Parking Access Control Woof, Selvi Leoni; Patiran, Abdul Zaid; Sumendap, Andreas Leonardo
MALCOM: Indonesian Journal of Machine Learning and Computer Science Vol. 6 No. 3 (2026): MALCOM July 2026
Publisher : Institut Riset dan Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/malcom.v6i3.2826

Abstract

Pengelolaan parkir secara konvensional masih memiliki keterbatasan dalam pemantauan kendaraan, pengendalian akses, dan pencatatan data secara terstruktur. Sistem dirancang untuk menangkap citra objek melalui ESP32-CAM, memproses deteksi motor menggunakan YOLOv3 pada Python, mengendalikan portal melalui motor servo, menyalakan lampu kilat pada kondisi tertentu, serta menyimpan data hasil deteksi ke basis data MySQL. Pengujian sistem dilakukan pada area parkir Lapangan Borasi, Manokwari, Papua Barat. Kontribusi penelitian ini terletak pada integrasi deteksi kendaraan roda dua berbasis YOLOv3 dengan sistem kontrol portal otomatis, pencahayaan adaptif menggunakan flash ESP32-CAM, serta penyimpanan data deteksi secara real-time ke basis data MySQL dalam satu sistem IoT yang terintegrasi. Pengujian dilakukan menggunakan 19 data uji yang terdiri atas 15 motor dan 4 objek bukan motor. Hasil pengujian menunjukkan bahwa 13 motor berhasil terdeteksi, sedangkan 2 motor tidak terdeteksi pada malam hari akibat rendahnya pencahayaan. Pada objek bukan motor, 2 mobil dan 1 orang berhasil dikenali sebagai bukan motor, sedangkan 1 sepeda salah terdeteksi sebagai motor. Berdasarkan confusion matrix, sistem memperoleh akurasi sebesar 84,21%, presisi motor sebesar 92,86%, dan recall sebesar 86,67%. Hasil ini menunjukkan bahwa sistem mampu melakukan deteksi motor dan pengendalian portal secara otomatis, meskipun masih dipengaruhi oleh kondisi pencahayaan dan kemiripan bentuk objek.
Klasifikasi Hate Speech dan Offensive Language Menggunakan Hybrid RoBERTa dan XGBoost dengan Optimasi Hyperparameter: Hate Speech and Offensive Language Classification Using Hybrid RoBERTa and XGBoost with Hyperparameter Optimization Jauhari, Najwa; Agustian, Surya; Syafria, Fadhilah; Affandes, Muhammad
MALCOM: Indonesian Journal of Machine Learning and Computer Science Vol. 6 No. 3 (2026): MALCOM July 2026
Publisher : Institut Riset dan Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/malcom.v6i3.2834

Abstract

Ujaran kebencian dan bahasa ofensif di media sosial merupakan masalah serius yang membutuhkan deteksi otomatis yang akurat. Penelitian ini mengusulkan pendekatan hybrid yang menggabungkan model Robustly Optimised BERT Approach (RoBERTa) berbasis Twitter (cardiffnlp/twitter?roberta?base?offensive) sebagai ekstraktor fitur dengan algoritma XGBoost untuk klasifikasi pada dataset Hate Speech and Offensive Content Identification (HASOC) 2021 berbahasa Inggris. Kebaruan penelitian ini terletak pada integrasi optimasi hyperparameter Optuna, Multi?Seed Ensemble, Division Calibration, dan oversampling pada ruang fitur embedding yang belum pernah diterapkan secara bersamaan dalam satu pipeline pada HASOC 2021. Dua tugas diselesaikan : Task 1A adalah klasifikasi biner untuk membedakan konten Hate and Offensive (HOF) dari yang tidak (NOT). Task 1B adalah klasifikasi multi-kelas yang membagi tweet menjadi empat kategori: (ujaran kebencian terhadap kelompok) HATE, bahasa ofensif terhadap individu (OFFN), kata kasar tanpa target spesifik (PRFN), dan konten aman (NONE).  Ketidakseimbangan kelas ditangani dengan oversampling pada ruang fitur, dan optimasi hyperparameter dilakukan untuk meningkatkan performa. Hasil evaluasi pada data uji menunjukkan Macro F1?Score sebesar 0,8083 untuk Task 1A dan 0,6541 untuk Task 1B. Perbandingan dengan papan peringkat HASOC 2021 menunjukkan bahwa skor tersebut sebanding dengan tim peringkat 5 (Task 1A) dan peringkat 3 (Task 1B) yang berbasis BERT murni.
Real-Time Indonesian Sign Language Recognition Using Long Short-Term Memory and IndoBERT Contextual Modeling Maritza, Amanda Betania; Ardiansyah, Rizka
MALCOM: Indonesian Journal of Machine Learning and Computer Science Vol. 6 No. 3 (2026): MALCOM July 2026
Publisher : Institut Riset dan Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/malcom.v6i3.2839

Abstract

Communication barriers between deaf and hearing individuals remain a major challenge in inclusive education and social interaction. Most Indonesian Sign Language (Sistem Isyarat Bahasa Indonesia = SIBI) recognition systems focus solely on isolated gesture classification, without contextual understanding. This study proposes a real-time SIBI recognition system integrating LSTM-based temporal modeling and IndoBERT contextual language modeling. Hand keypoints were extracted from gesture sequences and processed using Long Short-Term Memory (LSTM) to recognize dynamic alphabet gestures. The predicted letter sequences were refined using a Trie-based lexical filter and IndoBERT to generate contextually appropriate word predictions. The dataset consisted of 780 gesture sequences representing 26 alphabet classes. The results showed that the system achieved 89.42% accuracy, 91.57% precision, 89.42% recall, and 88.74% F1-score while maintaining real-time performance at an average processing speed of 249.18 FPS and a latency of 129.46 ms per sample. Statistical evaluation confirmed that camera distance significantly affected recognition performance, while integrating IndoBERT improved contextual word-prediction accuracy from 18.2% to 58.64% (3.2× improvement). McNemar analysis further verified that the improvement was statistically significant, highlighting the effectiveness of contextual language modeling for enhancing semantic interpretation SIBI recognition. The proposed framework improves semantic interpretation and real-time usability for SIBI-based assistive communication systems.
Multi-Label Text Classification in the Translated Hadith of Bukhari Using A Backpropagation Neural Network Mislianingsih, Yuliana; Harahap, Nazruddin Safaat; Abdillah, Rahmad; Candra, Reski Mai
MALCOM: Indonesian Journal of Machine Learning and Computer Science Vol. 6 No. 3 (2026): MALCOM July 2026
Publisher : Institut Riset dan Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/malcom.v6i3.2865

Abstract

Hadith is the second primary source of Islamic law. This study aims to classify the translations of Sahih al-Bukhari Hadith into three thematic categories simultaneously, namely recommendations, prohibitions, and information, using a multi-label classification approach with the Backpropagation Neural Network (BPNN) method. The dataset consists of 7,000 labeled instances. The preprocessing stage includes case folding and text cleaning without stopword removal or stemming to preserve the original meaning of the text. The novelty of this study lies in the comprehensive exploration of various model configurations, including variations in hidden layer architecture, learning rate values, and n-gram features, to identify the optimal network architecture. Feature extraction was performed using Term Frequency–Inverse Document Frequency (TF-IDF) with n-gram configurations of (1,1) and (1,2) and a maximum of 7,000 features. Three variations of the BPNN architecture (H1, H2, and H3) were evaluated using 10-fold cross-validation. The experimental results show that the simplest architecture, consisting of a single hidden layer with 64 neurons, a learning rate of 0.001, and an n-gram configuration of (1,1), achieved the best performance, with macro accuracy of 91.43%, F1-score of 89.59%, and Hamming loss of 0.0857.
Palm Oil Production Prediction Using a Multi-Architecture Deep Learning Approach Based on Long Short-Term Memory and Gated Recurrent Unit Syahidah, Hanifatus; Mustakim, Mustakim; Hartono, Hartono
MALCOM: Indonesian Journal of Machine Learning and Computer Science Vol. 6 No. 3 (2026): MALCOM July 2026
Publisher : Institut Riset dan Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/malcom.v6i3.2924

Abstract

Palm oil production in Riau Province fluctuates due to seasonal factors and complex temporal patterns, making accurate forecasting with conventional statistical methods challenging. This study aims to compare the performance of six deep learning architectures Long Short-Term Memory (LSTM), Bidirectional LSTM (Bi-LSTM), Stacked LSTM, Gated Recurrent Unit (GRU), Bidirectional GRU (Bi-GRU), and Stacked GRU in predicting monthly palm oil production and identifying the most effective model. Two datasets obtained from PT Perkebunan Nusantara (PTPN) and Badan Pusat Statistik (BPS), covering the period from 2014 to 2023, were employed in this study. All models were evaluated using Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE). The experimental results demonstrate that the GRU model consistently outperformed the other architectures, achieving the highest prediction accuracy of 95.84% and the lowest MAPE of 4.16% on the BPS dataset, while also producing the best overall performance on the PTPN dataset. These findings indicate that the research objective was successfully achieved and demonstrate that GRU provides a reliable, computationally efficient approach for forecasting palm oil production with limited time-series data. 
Implementation of Time Series Forecasting for Inflation Prediction in Indonesia Mustakim Mustakim; Wahyu Eka Putra; Hartono Hartono
MALCOM: Indonesian Journal of Machine Learning and Computer Science Vol. 6 No. 2 (2026): MALCOM April 2026
Publisher : Institut Riset dan Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/malcom.v6i2.2172

Abstract

Inflation is a crucial macroeconomic indicator that reflects economic stability and influences sectors such as consumption, investment, and policy-making. This study aims to implement and compare three time series forecasting models: Seasonal Autoregressive Integrated Moving Average (SARIMA), Support Vector Regression (SVR), and Long Short-Term Memory (LSTM) to predict inflation in Indonesia. The study utilizes monthly inflation data from Bank Indonesia (2003–2024) and Consumer Price Index (CPI) data from Statistics Indonesia (2003–2019). Model performance is evaluated using Mean Absolute Error (MAE), Mean Squared Error (MSE), and Root Mean Squared Error (RMSE). The results indicate that SVR achieves the best performance in predicting inflation, with MAE of 1.53, MSE of 2.72, and RMSE of 1.64, demonstrating its effectiveness in capturing nonlinear patterns. Meanwhile, SARIMA provides the most accurate predictions for CPI, with MAE of 11.55, MSE of 191.76, and RMSE of 13.84. LSTM shows competitive performance but is less consistent compared to the other models. These findings highlight the importance of selecting appropriate models based on data characteristics to improve forecasting accuracy and support economic decision-making.
Klasifikasi Jenis Sayuran Berbasis Citra Menggunakan Convolutional Neural Network dengan Penyediaan Informasi Kandungan Nutrisi: Image-Based Vegetable Type Classification Using Convolutional Neural Network with Nutritional Content Information Provision Intan Idaman Halawa; Choirul Basir
MALCOM: Indonesian Journal of Machine Learning and Computer Science Vol. 6 No. 2 (2026): MALCOM April 2026
Publisher : Institut Riset dan Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/malcom.v6i2.2267

Abstract

Banyak jenis sayuran memiliki tampilan yang mirip dalam hal bentuk, warna, dan ukuran, sehingga sulit dibedakan secara manual karena membutuhkan waktu lama dan rentan terhadap kesalahan. Selain itu, rendahnya pengetahuan masyarakat tentang nutrisi sayuran yang sesuai dengan kondisi kesehatan pribadi menjadi tantangan tersendiri. Penelitian ini merancang model klasifikasi sayuran berbasis citra menggunakan deep learning dengan metode Convolutional Neural Network (CNN) berbasis TensorFlow yang diintegrasikan dengan penyediaan informasi kandungan nutrisi secara otomatis. Dataset mencakup 15 jenis sayuran umum: brokoli, capsicum, kacang, kembang kol, kentang, kubis, labu botol, labu pahit, lobak, mentimun, pepaya, terong, tomat, waluh, dan wortel, dengan total 21.000 citra yang dibagi menjadi data latih (training set 15.000 citra), data validasi (validation set 3.000 citra), dan data uji (test set 3.000 citra). Model CNN dengan arsitektur sequential 8 lapisan dilatih dengan variasi parameter masukan, epoch, dan metrik evaluasi. Model terbaik menunjukkan training accuracy 92,8%, validation accuracy 95,83%, dan testing accuracy 95,67%. Evaluasi menggunakan confusion matrix menghasilkan akurasi keseluruhan 96%, presisi tertinggi 99%, recall tertinggi 100%, dan F1-score tertinggi 100%. Model yang diusulkan berpotensi dimanfaatkan untuk pelacakan gizi, kontrol kualitas makanan, dan pengembangan produk pangan.
K-Means and Decision Tree Models for Analysis Students Perception of Digital Marketing Tesdiq Prigel Kaloka; Ahmadi Marta; Mariyam Mariyam; Nadila Deryza Syofrin
MALCOM: Indonesian Journal of Machine Learning and Computer Science Vol. 6 No. 2 (2026): MALCOM April 2026
Publisher : Institut Riset dan Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/malcom.v6i2.2360

Abstract

Amid intensifying competition among educational institutions and the rapid expansion of digital platforms, schools must adopt effective digital marketing strategies to attract prospective students. This research examines students’ perceptions of the implementation of digital marketing at the Global Islamic Boarding School (GIBS) in Barito Kuala Regency, South Kalimantan, Indonesia. Data were collected from 95 students using a structured questionnaire comprising 20 variables related to digital marketing and word-of-mouth communication. A hybrid analytical framework integrating K-Means Clustering and Decision Tree analysis was employed to identify perception-based segments and determine key differentiating indicators. The Elbow method indicated an optimal three-cluster solution, resulting in highly positive, negative, and moderate perception groups. Decision Tree results revealed that engaging content (B7), social media stability (B12), and invitation to attend school (C19) were the most influential variables distinguishing clusters, with satisfactory classification performance supporting the robustness of the segmentation. The findings suggest that emotionally engaging and experientially rich content plays a more decisive role in shaping student perceptions than purely technical aspects of digital media management. Accordingly, schools should optimize their digital marketing strategies by emphasizing authentic student experiences, maintaining consistent, stable online communication, and incorporating persuasive calls to action to enhance prospective students’ interest.
UI/UX Desain Mobile Apps Skin Check untuk Identifikasi Jenis Kulit dan Rekomendasi Produk Skincare: UI/UX Design of Mobile Apps Skin Check to Identify Skin Type and Recommend Skincare Products Nava Gia Ginasta; Fariz Faqih Firdaus
MALCOM: Indonesian Journal of Machine Learning and Computer Science Vol. 6 No. 2 (2026): MALCOM April 2026
Publisher : Institut Riset dan Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/malcom.v6i2.2383

Abstract

Penelitian ini bertujuan mengevaluasi implementasi User-Centered Design (UCD) pada aplikasi identifikasi jenis kulit dan rekomendasi skincare, serta menganalisis pengaruh usability terhadap trust dan intention to use. Penelitian menggunakan pendekatan kuantitatif dengan task-based usability testing dan kuesioner. Sebanyak 45 responden (usia 18–30 tahun) berpartisipasi dalam pengujian. Instrumen yang digunakan meliputi System Usability Scale (SUS), Trust Scale, dan Intention to use Scale. Data objektif yang diukur mencakup Task Completion Rate, waktu penyelesaian, dan Error Rate. Hasil menunjukkan Task Completion Rate sebesar 92%, Error Rate sebesar 5%, dan rata-rata waktu penyelesaian sebesar 2,4 menit. Skor SUS sebesar 84,6 termasuk kategori excellent. Skor trust dan intention to use masing-masing sebesar 4,3 dan 4,5 (skala 1–5). Analisis korelasi Pearson menunjukkan hubungan positif yang kuat antara usability dan trust (r = 0,71), serta antara trust dan intention to use (r = 0,76). Temuan ini menunjukkan bahwa peningkatan usability berkontribusi secara signifikan terhadap kepercayaan dan niat penggunaan aplikasi. Penelitian ini menegaskan pentingnya integrasi UCD dan evaluasi usability berbasis data empiris dalam pengembangan aplikasi skincare digital.

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