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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
 48   49   50   51   52 
Deteksi Kondisi Gigi Anak pada Radiografi Panoramik Menggunakan YOLOv8 dan Teknik Peningkatan Citra: Detection of Children's Dental Conditions in Panoramic Radiography Using YOLOv8 and Image Enhancement Techniques Khoifah Inda Maula; Chastine Fatichah; Hilya Tsaniya Ismet
MALCOM: Indonesian Journal of Machine Learning and Computer Science Vol. 6 No. 1 (2026): MALCOM January 2026
Publisher : Institut Riset dan Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/malcom.v6i1.2438

Abstract

Citra panoramik gigi merupakan teknik radiografi yang memberikan gambaran menyeluruh terhadap struktur gigi, rahang, serta jaringan pendukung lainnya dalam satu citra, sehingga banyak digunakan untuk diagnosis awal dan perencanaan perawatan, khususnya pada pasien anak-anak. Namun, karakteristik gigi anak yang unik seperti keberadaan gigi campuran dan perubahan posisi gigi yang dinamis mengakibatkan interpretasi citra menjadi lebih kompleks. Selain itu, kualitas citra yang kurang optimal seperti kontras rendah dan distribusi sinar-X yang tidak merata dapat menghambat proses deteksi secara akurat. Penelitian ini bertujuan meningkatkan kualitas citra serta melakukan deteksi otomatis citra panoramik gigi anak menggunakan You Only Look Once (YOLO) yang dikenal unggul dalam kecepatan dan akurasi deteksi objek. Proses peningkatan citra dilakukan dengan tiga teknik, yaitu Histogram Equalization (HE), Contrast Limited Adaptive Histogram Equalization (CLAHE), dan gamma correction. Hasil pengujian menunjukkan bahwa penerapan CLAHE memberikan performa deteksi terbaik dibandingkan dengan metode HE maupun gamma correction. Berdasarkan analisis metrik evaluasi, penggunaan CLAHE terbukti paling optimal dalam meratakan kontras lokal dan menekan noise, sehingga YOLOv8 dapat mengekstraksi fitur gigi anak yang kompleks. Sebagai kesimpulan, kombinasi metode prapemrosesan CLAHE dan model deteksi YOLOv8 merupakan pendekatan yang paling efektif untuk mengatasi permasalahan kualitas citra dan direkomendasikan untuk pengembangan sistem diagnosis otomatis citra panoramik gigi anak.
Systematic Review and Bibliometric Mapping on Image Processing in Electronic Health Records Amir Hamzah Dinnillah; Fikri Maulana
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.v6i2.2462

Abstract

The integration of medical image processing techniques into Electronic Health Records (EHR) has become a vital element in the transformation of modern digital healthcare services. This study aims to conduct a Systematic Literature Review (SLR) and bibliometric analysis to evaluate current research trends, patterns, and gaps from 2021 to 2025. Using the PRISMA framework and data from the Scopus database, this study analyzes 23 selected articles visualized using VOSviewer software. The results reveal a significant surge in publications, driven by the adoption of Artificial Intelligence (AI) and Deep Learning, which have been proven to improve diagnostic accuracy and facilitate early detection of critical diseases. Although these technologies support better clinical decision-making, major challenges related to system interoperability, data standardization, and patient privacy security remain substantial obstacles that need to be overcome. The study also highlights the role of emerging technologies such as the Internet of Medical Things (IoMT) and blockchain as potential solutions for data security. In conclusion, this research provides strategic guidance for developers and policymakers to create a more interoperable, secure, and efficient EHR ecosystem.
Systematic Literature Review of Transfer Learning for Pneumonia Classification in Chest X-Rays Erlan Bachtiar; Amir Hamzah Dinnillah; Yan Rianto
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.v6i2.2470

Abstract

Diagnosis of pneumonia through manual interpretation of Chest X-Ray (CXR) images is often hampered by observer subjectivity and radiologist fatigue, which can potentially lead to misdiagnosis. This study aims to evaluate the effectiveness and development trends of Transfer Learning techniques, particularly the ResNet, VGG, and DenseNet architectures, in pneumonia classification through the Systematic Literature Review (SLR) method. In accordance with the PRISMA protocol, the search was conducted in the Scopus database from 2021 to 2025, yielding 76 articles that met the inclusion criteria. Bibliometric analysis shows that the publication trend, initially triggered by the urgency of the pandemic, has now shifted to a phase of technological maturity, with a focus on integrating Explainable AI (XAI) to address black-box problems. Geographically, research contributions are dominated by institutions in Asia and the Middle East. The main findings confirm that Transfer Learning can significantly improve diagnostic accuracy and initial screening efficiency compared to conventional methods. However, challenges such as data imbalance and the need for clinical validation remain obstacles. This study concludes that the future of computer-assisted diagnosis systems depends on improving model transparency to support precise and reliable Clinical Decision Support Systems (CDSS).
Implementation of Machine Learning to Predict The Timeliness of Graduation of Employees on Study Assignment at Company X Parenda Rizkya Permata; Imam Yuadi
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.v6i2.2474

Abstract

The energy transition requires workers in the energy sector who have relevant skills that can be applied in the future. Company X implements a study assignment program to improve its employees' skills, but delays in completing their studies hinder their readiness to enter the workforce. Identifying the factors that influence graduation timeliness can improve the program's effectiveness. This study aims to develop a predictive model to determine whether employees in Company X's work-study program will graduate on time. The main purpose of this model is to provide early warnings about employees at risk of delays, enabling more targeted interventions to improve human resource management. We applied the CRISP-DM framework and used Machine Learning to analyze data from 317 employees who participated in the study program. Four machine learning algorithms were tested, namely Gradient Boosting, Decision Tree, Random Forest, and Naive Bayes. 17 factors were trained to cover academic, demographic, and administrative aspects to predict timely graduation. Among the algorithms tested, Gradient Boosting showed the best performance with an AUC of 0.956 and an accuracy of 0.909. These results were supported by high ROC and confusion matrix values, indicating the model's excellent predictive ability. 
Unsupervised Text Mining of Employee Feedback for Identifying Organizational Strengths and Improvement Areas Febri Ari Wicaksono; Imam Yuadi
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.v6i2.2482

Abstract

Employee feedback provides rich signals about organizational performance, yet its free-text format makes systematic analysis at scale difficult. This study proposes an unsupervised text mining workflow in Orange Data Mining to extract actionable themes from continuous employee comments by separating two semantic polarities: strength feedback (“What went well?”) and improvement feedback (“What could be improved?”). After cleaning and Indonesian-language preprocessing (Sastrawi stemming, custom stopwords), 3,406 strength and 3,172 improvement entries were represented using TF–IDF. Improvement feedback was clustered using K-Means and assessed with silhouette-based validation, while both feedback types were explored using LDA topic modeling supported by topic coherence checks for interpretability. The results reveal recurring organizational themes related to goal execution and performance, supervision, communication/coordination, and motivation, with notable vocabulary overlap between strengths and areas for improvement. Scientifically, this work demonstrates how polarity-aware unsupervised analytics improves interpretability compared to treating feedback as a single corpus, and practically, it provides a scalable way for managers to transform unstructured feedback into structured insights for targeted improvement initiatives.
Implementasi Metode Hybrid Backpropagation Neural Network dan Particle Swarm Optimization untuk Prediksi Konsumsi Listrik Rumah Tangga Berdasarkan Golongan Tarif : Implementation of Hybrid Backpropagation Neural Network and Particle Swarm Optimization for Predicting Household Electricity Consumption Based On Tariff Categories Yuni Artha Chyntia Saragih; Erma Suryani
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.v6i2.2519

Abstract

Konsumsi listrik pada sektor rumah tangga terus meningkat seiring dengan pertumbuhan penduduk dan perkembangan sosial ekonomi. Penelitian ini mengusulkan model peramalan hybrid yang mengintegrasikan Backpropagation Neural Network (BPNN) dengan Particle Swarm Optimization (PSO) untuk meningkatkan akurasi prediksi konsumsi listrik rumah tangga berdasarkan golongan tarif. Data historis penjualan listrik bulanan periode 2020 sampai dengan 2024 digunakan sebagai dataset, yang mencakup jumlah pelanggan, daya tersambung, dan konsumsi energi. Hasil penelitian menunjukkan bahwa model BPNN–PSO memiliki kinerja yang lebih baik dibandingkan dengan BPNN murni. Proses optimasi berhasil menurunkan nilai Mean Absolute Percentage Error (MAPE) dari 57,97% menjadi 46,38% serta meningkatkan nilai koefisien determinasi (R²) dari –0,2403 menjadi 0,1831. Model yang diusulkan kemudian digunakan untuk memproyeksikan kebutuhan listrik periode 2025–2029 dan menunjukkan adanya tren pertumbuhan yang konsisten. Temuan ini membuktikan bahwa pendekatan hybrid BPNN–PSO dapat menjadi alat peramalan yang lebih andal dalam mendukung perencanaan dan pengambilan keputusan di sektor ketenagalistrikan.
Klasifikasi Data Pariwisata Berkelanjutan Menggunakan Decision Tree dengan Equal Width dan Logaritma Binning : Decision Tree Classification Using Equal Width and Logarithmic Binning for Sustainable Tourism Data Fahri Alviansyah; Mukti Adi Azhari; Attar Raihan Nazhif; Suharto Suharto; Denny Saryanto; Kusrini Kusrini
MALCOM: Indonesian Journal of Machine Learning and Computer Science Vol. 6 No. 1 (2026): MALCOM January 2026
Publisher : Institut Riset dan Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/malcom.v6i1.2520

Abstract

Pengelolaan data rekomendasi pariwisata memerlukan pemodelan prediktif yang akurat untuk mengklasifikasikan target rekomendasi berdasarkan perilaku wisatawan. Namun, variabel numerik seperti Rekomendasi Score seringkali memiliki distribusi data yang tidak merata (skewed), yang dapat memengaruhi performa algoritma pembelajaran mesin seperti Decision Tree. Penelitian ini bertujuan untuk membandingkan efektivitas dua teknik preprocessing dengan diskritisasi, yaitu Equal Width Binning (EWB) dan Logarithmic Binning (LB), dalam meningkatkan kinerja model klasifikasi. Metodologi penelitian ini mencakup beberapa tahapan preprocessing data, antara lain handling missing value, serta ekstraksi fitur temporal dari data tanggal perjalanan. Data kemudian diproses menggunakan dua skenario binning yang berbeda sebelum dilatih menggunakan algoritma Decision Tree. Hasil penelitian dievaluasi menggunakan metrik Akurasi, Presisi, Recall, dan F1-Score. Hasil perbandingan menunjukkan bahwa Equal Width Binning nilai akurasi sebesar 82 %, dan Logarithmic Binning memberikan nilai akurasi sebesar 90%. Diskritisasi melalui logaritma bining mampu mengurangi kedalaman pohon (tree depth) dan mencegah overfitting, sehingga menghasilkan model yang lebih tangguh dalam memprediksi target rekomendasi pariwisata.
LiDAR and Visual Perception-Based Indoor Semantic Mapping: Comparative Study of GMapping and SLAM Toolbox Muhammad Salam Pararta Saragi; Deden Pradeka; Anugrah Adiwilaga; Dyah Kusuma Dewi; Roni Permana Saputra
MALCOM: Indonesian Journal of Machine Learning and Computer Science Vol. 6 No. 1 (2026): MALCOM January 2026
Publisher : Institut Riset dan Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/malcom.v6i1.2521

Abstract

This study investigates semantic embedding strategies for indoor mapping by comparing Trajectory-Based Payload Embedding (TPE) in GMapping and Pose-Based Payload Embedding (PPE) in SLAM Toolbox. A custom Turtlebot3 platform equipped with a 2D LiDAR and six RGB cameras was used in the Gazebo simulation to acquire geometric and visual data. Object segmentation results from YOLOv11 were integrated into occupancy grids using two distinct embedding workflows: scan-level batch attachment in TPE and point-level graph persistence in PPE. Performance evaluation employed two metrics: pixel-level accuracy and time cost under three varied velocity conditions, followed by a comparative analysis. Results show that PPE achieved higher accuracy (mean 86.83%) and lower variability, while maintaining negligible time cost (<0.5 ms). TPE, although simpler to implement, exhibited greater sensitivity to motion dynamics and higher computational variability (average 350.47 ms). These findings highlight a trade-off between accuracy and efficiency, suggesting PPE as the more suitable approach for real-time semantic SLAM, while TPE remains useful for lightweight integration scenarios. Beyond quantitative results, the study contributes methodological insights into how embedding granularity and persistence affect semantic consistency, offering guidance for future implementations in both simulated and real-world robotic navigation.
Analisis Sentimen Berbasis Aspek pada Ulasan Pariwisata Menggunakan Varian Algoritma K-Nearest Neighbor: Aspect-Based Sentiment Analysis of Tourism Reviews Using Variants of the K-Nearest Neighbor Algorithm Anastasya Nurfitriyani Hidayat; Ahmad Luky Ramdani; Luluk Muthoharoh
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.v6i2.2522

Abstract

Sektor pariwisata merupakan sektor kunci dalam pembangunan daerah dan kesejahteraan masyarakat. Ulasan pengunjung penting karena memberikan informasi yang membantu meningkatkan kualitas objek wisata. Penelitian ini bertujuan untuk melakukan analisis sentimen berbasis aspek pada objek wisata di Kabupaten Samosir dengan fokus pada tiga aspek utama, yaitu atraksi (attractions), amenitas (amenity), dan aksesibilitas (accessibility) berdasarkan ulasan pengunjung di Google Maps. Berdasarkan hasil eksperimen aspek objek wisata, Modified K-Nearest Neighbor (MKNN) mencapai akurasi 55,50%, presisi 62,28%, recall 80,03%, dan F1-Score 59,96%, sedangkan K-Nearest Neighbor (KNN) mencapai akurasi 74%, presisi 65,17%, recall 84,64%, dan F1-Score 70,45%. Untuk aspek kenyamanan, MKNN mencapai akurasi 89,50%, presisi 87,71%, recall 85,19%, dan F1-Score 86,27%, sedangkan KNN menghasilkan akurasi 92,50%, presisi 93,54%, recall 86,99%, dan F1-Score 89,83%. Pada aspek Aksesibilitas, MKNN memperoleh akurasi 85,50% dengan presisi 77,53%, recall 72,44%, dan F1-Score 74,71%, sedangkan KNN mencapai akurasi 86,50%, presisi 79,02%, recall 70,59%, dan F1-Score 74,05%. Dari sini terlihat bahwa model KNN masih menunjukkan performa yang lebih unggul dibandingkan dengan MKNN pada ketiga aspek yang dianalisis.
Analisis Sentimen Pengguna Aplikasi Jamsostek Mobile Berdasarkan Ulasan Google Play Store Menggunakan Algoritma Support Vector Machine dan Naive Bayes: Sentiment Analysis of Jamsostek Mobile Application Reviews on Google Play Store Using Support Vector Machine and Naive Bayes Algorithms Tria Setyani; Kevinda Sari; Helma Nopijani Heidy; Ryan Randy Suryono
MALCOM: Indonesian Journal of Machine Learning and Computer Science Vol. 6 No. 1 (2026): MALCOM January 2026
Publisher : Institut Riset dan Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/malcom.v6i1.2526

Abstract

Penelitian ini bertujuan untuk menganalisis sentimen ulasan pengguna aplikasi Jamsostek Mobile (JMO) yang tersedia pada Google Play Store menggunakan algoritma Support Vector Machine (SVM) dan Naive Bayes. Data yang digunakan sebanyak 6.000 ulasan pengguna yang dikumpulkan melalui teknik web scraping. Tahapan penelitian meliputi preprocessing teks (cleaning, case folding, normalisasi, tokenizing, stopword removal, dan stemming), pembobotan fitur menggunakan Term Frequency–Inverse Document Frequency (TF-IDF), pelabelan data dengan metode lexicon-based, serta klasifikasi sentimen ke dalam tiga kelas, yaitu positif, negatif, dan netral. Evaluasi performa model dilakukan menggunakan confusion matrix dan metrik akurasi, precision, recall, dan F1-score. Hasil penelitian menunjukkan bahwa algoritma SVM menghasilkan performa yang lebih unggul dengan nilai akurasi sebesar 88,9%, sedangkan Naive Bayes memperoleh akurasi sebesar 64,1%. SVM juga menunjukkan nilai F1-score yang lebih konsisten pada seluruh kelas sentimen dibandingkan Naive Bayes. Dengan demikian, algoritma SVM terbukti lebih efektif dan andal dalam mengklasifikasikan sentimen ulasan pengguna aplikasi JMO.

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