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INSTITUT RISET DAN PUBLIKASI INDONESIA Jl. Tuah Karya Ujung C7. Kel. Tuah Madani Kec. Tampan Kota Pekanbaru - Riau
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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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Multi-Commodity Food Price Forecasting Ahead of Eid Al-Fitr in Indonesia (2026–2030): A Comparative Study of Machine Learning Algorithms Using Time-Series Data Murtani Murtani; Dewi Kobi; Imelda Imelda
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.v6i1.2572

Abstract

Food price volatility during the Eid Al-Fitr season poses a recurring socioeconomic challenge in Indonesia, significantly affecting household purchasing power and national food security. This study develops a predictive framework for forecasting staple food price increases ahead of Eid Al-Fitr for the period 2026–2030 using three machine learning algorithms: Random Forest (RF), Long Short-Term Memory (LSTM), and Gradient Boosting Regression (GBR). The dataset comprises multi-commodity time-series records of eleven essential commodities, including rice, chicken, beef, eggs, shallots, garlic, chili peppers, cooking oil, sugar, wheat flour, and soybeans, collected from the Indonesian National Strategic Food Price Information Center (PIHPS) spanning January 2015 to December 2025. Exogenous features, including inflation rate, USD/IDR exchange rate, fuel price index, and seasonal indicators, were incorporated. A walk-forward validation scheme with a strict chronological train validation test split was employed to prevent data leakage, and a recursive multi-step forecasting strategy was adopted for generating the 2026–2030 predictions. The results demonstrate that LSTM achieved the highest predictive accuracy with a Mean Absolute Percentage Error (MAPE) of 4.32%, followed by GBR (5.87%) and RF (7.14%). The model forecasts an average price surge of 12.6–18.4% across key commodities during the 30-day pre-Eid window for 2026–2030.
Time Series Approach for Analysis and Prediction of Malware Trends Based on Open Source Intelligence Tommy Nugraha Manoppo; Abdul Gani Fadhlulrahman; Yudi Prayudi
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.v6i2.2580

Abstract

The growing threat of malware requires monitoring approaches that provide a continuous, measurable overview of threat trends. This study proposes an open-source intelligence-based malware trend monitoring system using time series forecasting and anomaly signaling. Data are obtained from the MalwareBazaar repository and processed into a daily malware activity time series, with contextual aggregation to identify dominant malware families. The AutoRegressive Integrated Moving Average (ARIMA) model is applied for short-horizon prediction, and statistical anomaly detection is implemented using Z-scores to flag activity deviations. The system is delivered as an interactive dashboard that visualizes daily malware trends, dominant malware families, forecasting outputs, and anomaly indicators. Experimental results show that ARIMA(2,0,0) provides measurable improvement over a naive persistence baseline, reducing MAE from 102.75 to 92.67 and RMSE from 125.13 to 109.73, while improving sMAPE from 26.74% to 24.48% on the evaluation window. The novelty of this work lies in integrating an OSINT malware repository signal, benchmarked statistical forecasting, quantitative evaluation, and anomaly signaling into a single monitoring dashboard. Practically, the system can support SOC analysts by providing early-warning cues for monitoring prioritization and support digital forensic practitioners by strengthening digital forensic readiness through earlier visibility emerging malware activity dynamics and dominant artifact categories.
Comparative Analysis of ANN, 1D-CNN, and LSTM for Multi-Label Action Prediction in IoT-Based Hydroponic Control Systems Reza Octaviany; Suhendro Yusuf Irianto
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.v6i2.2581

Abstract

Hydroponic cultivation requires precise and adaptive fertility control to maintain optimal plant growth. Conventional rule-based systems operate reactively and often fail to capture the multivariate and temporal dynamics of sensor data. Unlike previous studies that primarily focus on single-parameter forecasting, this study reformulates hydroponic automation as a multi-label actuator prediction problem, aiming to replicate and generalize rule-based control mechanisms using data-driven learning. A comparative analysis of Artificial Neural Network (ANN), one-dimensional Convolutional Neural Network (1D-CNN), and Long Short-Term Memory (LSTM) models was conducted to simultaneously predict six actuator states in an IoT-based hydroponic system. The dataset consists of 1,152 real multivariate time-series samples collected sequentially at 5-minute intervals, comprising six sensor features and six binary actuator labels derived from agronomic standards. Preprocessing includes Gaussian jitter-based augmentation, Z-score normalization, and sliding-window modeling (window size = 5). Data were split chronologically into 80% for training and 20% for testing, with 10% for validation. Results show that LSTM achieved the highest performance (accuracy up to 0.98; F1-score up to 0.95), demonstrating superior temporal modeling capability. Threshold optimization improved minority-actuator detection, enabling reliable, adaptive hydroponic control.
Tinjauan Literatur Sistematis Tentang Algoritma Deteksi Objek untuk Sistem Otonom: Evaluasi Arsitektur CNN, Tantangan Dataset, dan Strategi Implementasi: Systematic Literature Review on Object Detection Algorithms for Autonomous Systems: Evaluation of CNN Architectures, Dataset Challenges, and Implementation Strategies Mohammad Yasser Arafat; Muhammad Bagus Andra
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.v6i2.2584

Abstract

Sistem deteksi objek berbasis Convolutional Neural Network (CNN) menjadi komponen penting dalam pengembangan teknologi otonom seperti kendaraan tanpa pengemudi, robot mobile, dan sistem pengawasan cerdas. Penelitian ini melakukan tinjauan literatur sistematis menggunakan kerangka PRISMA 2020 untuk mengevaluasi arsitektur CNN, tantangan dataset, serta strategi implementasi pada sistem dengan keterbatasan komputasi. Dari 30 artikel yang diidentifikasi, hanya enam artikel primer berkualitas tinggi (2021–2025) yang memenuhi kriteria seleksi ketat dari berbagai basis data akademik. Hasil analisis menunjukkan bahwa arsitektur hybrid yang menggabungkan attention mechanism dan multi-scale feature pyramid networks memberikan performa terbaik, dengan model AttenRetina mencapai mAP 0.86 pada dataset KITTI. Tantangan utama dalam dataset meliputi deteksi objek kecil, latar belakang kompleks, oklusi parsial, serta variasi pencahayaan. Masalah ini diatasi melalui penggunaan dynamic loss functions dan teknik data augmentation. Untuk implementasi pada perangkat dengan sumber daya terbatas, arsitektur ringan seperti SSD MobileNetv2 dan YOLOv8-MobileNetV3 terbukti mampu memberikan keseimbangan optimal antara akurasi dan efisiensi. Secara keseluruhan, studi ini menawarkan panduan komprehensif bagi pengembang dalam memilih arsitektur, menyiapkan dataset, dan merancang strategi deployment sesuai kebutuhan aplikasi otonom.
The Lexicon-Based Sentiment Analysis for Stock Price Prediction in Islamic Fashion Industry Entis Sutisna; Rahma Dafitri; Dian Daryani
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.v6i2.2586

Abstract

This study develops a lexicon-based sentiment analysis method to examine the relationship between social media sentiment and stock price movements in Indonesia's Islamic fashion industry. We collected 6,087 social media comments from TikTok, Instagram, and Twitter over 396 days, alongside daily stock data from Elzatta (ZATA). Using an Indonesian fashion-specific lexicon with 42 positive and 38 negative indicators validated through inter-annotator agreement (Cohen's Kappa = 0.82), we employed ordinary least squares regression with heteroskedasticity-robust standard errors to test sentiment-return relationships. Results show no economically significant relationship between social media sentiment and stock returns (correlation = -0.06, p = 0.240), with models explaining only 2.09% of return variance. We identify three boundary conditions limiting sentiment-based prediction: (1) feature mismatch 82% of comments discuss product attributes rather than business fundamentals, (2) demographic disconnect social media users (78% female, aged 18-29) differ markedly from investors (68% male, aged 35-55), and (3) market microstructure constraints 43% zero-volume days and 2.3% bid-ask spreads impede price discovery. This study provides the first Indonesian fashion-specific sentiment lexicon and establishes actionable validation guidelines for practitioners: sentiment features must be verified for demographic alignment and market liquidity before deployment. For machine learning applications in emerging markets
Intelligent Automation of Linux System Administration via the NeuroSysAI Autonomous Agent: Security and Cost-Efficiency Analysis Auliya Ur Rahman Ash-Shidqi; Helmy Faisal Muttaqin
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.v6i2.2588

Abstract

Manual Linux system administration frequently suffers from operational inefficiencies and a high risk of human error. As IT infrastructure grows increasingly complex, traditional automation tools often lack the adaptability needed to handle dynamic troubleshooting. To address these limitations, this study introduces NeuroSysAI, an autonomous agent powered by Large Language Models (LLMs) specifically designed to automate server configuration, security management, and system monitoring in Ubuntu 22.04 LTS environments. Our approach implements a hybrid architecture that leverages the Mistral-Nemo API for complex reasoning alongside a local GPT-OSS model via Ollama to optimize operational costs. A primary contribution of this research is the integration of strict tool-use restrictions. This mechanism effectively mitigates the inherent risk of LLM hallucinations, ensuring that terminal command executions remain secure and controlled. Functional validation demonstrates that NeuroSysAI is highly reliable, achieving a 95.8% success rate in resolving administrative tasks with a robust system load capacity of 34 requests per second (RPS). Beyond technical performance, our threat modeling and cost evaluations confirm that this hybrid agent approach provides an optimal balance between infrastructure investment (CapEx) and operational efficiency (OpEx). Ultimately, NeuroSysAI offers system administrators a secure, adaptive, and economically viable solution for modernizing IT operations
Pendekatan Multi-Hirarki untuk Grading Sarang Burung Walet Berdasarkan Deteksi Bentuk dan Klasifikasi Warna: Multi-Hierarchical Machine Learning Approach for Edible Bird’s Nest Grading Based on Shape Detection and Color Classification Reinhard Alfaries Saemani; Danny Manongga; Hendry Hendry
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.v6i2.2594

Abstract

Sarang burung walet (SBW) merupakan komoditas bernilai ekonomi tinggi yang grading-nya masih banyak dilakukan secara manual sehingga rentan terhadap subjektivitas dan inkonsistensi, khususnya pada penilaian bentuk dan warna. Penelitian ini bertujuan mengembangkan sistem grading SBW yang objektif dan konsisten pada tahap bahan mentah melalui pendekatan multi-hirarki berbasis machine learning. Sistem terdiri dari dua level: deteksi bentuk menggunakan YOLOv8 untuk mengklasifikasikan tiga kategori utama (mangkok, oval, segitiga), dan analisis warna pada area sarang hasil cropping melalui segmentasi HSV lalu klasifikasi di ruang warna CIELAB. Model dilatih menggunakan 840 citra (3360 objek) dan diuji pada 120 citra (480 objek), dengan evaluasi performa deteksi dan klasifikasi secara kuantitatif. Hasil eksperimen menunjukkan YOLOv8 mencapai mAP@0,5 sebesar 99,5% dengan presisi dan recall sangat tinggi pada semua kelas bentuk, sedangkan analisis warna menghasilkan distribusi kuantitatif warna putih, kuning, dan kuning sekali tanpa tumpang tindih antar kelas. Pendekatan ini mengintegrasikan deteksi bentuk real-time berbasis YOLOv8 dengan klasifikasi warna perceptually uniform CIELAB, menghasilkan sistem grading SBW yang akurat, konsisten, dan aplikatif di industri, sekaligus menghadirkan metode terintegrasi yang lebih komprehensif dibandingkan penelitian sebelumnya yang hanya menekankan salah satu aspek, bentuk atau warna.
Penentuan Metode MOORA pada Sistem Pendukung Keputusan Penentuan Tempat Praktik Kerja Lapangan: Determination of the MOORA Method in the Decision Support System for Determining Field Work Practice Location Zilan Nazmi Dalimunthe; Arridha Zikra Syah; Febby Madonna Yuma
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.v6i2.2599

Abstract

Penentuan lokasi Praktik Kerja Lapangan (PKL) bagi siswa Sekolah Menengah Kejuruan (SMK) merupakan tahapan krusial untuk menyelaraskan kompetensi akademis dengan kebutuhan industri. Namun, proses pemilihan tempat PKL di SMK Yaspenda Pulau Rakyat masih bersifat konvensional dan subjektif, sehingga berisiko pada ketidaktepatan penempatan siswa. Penelitian ini bertujuan untuk mengimplementasikan Sistem Pendukung Keputusan (SPK) menggunakan metode Multi-Objective Optimization on the Basis of Ratio Analysis (MOORA) guna mengoptimalkan rekomendasi tempat PKL. Kriteria yang digunakan dalam penilaian meliputi presensi, keaktifan, serta minat dan bakat siswa. Hasil penelitian menunjukkan bahwa metode MOORA mampu melakukan pemeringkatan alternatif secara presisi, di mana SRH Training Center memperoleh nilai preferensi tertinggi (0,5596) sebagai lokasi paling direkomendasikan. Integrasi metode MOORA ke dalam sistem memberikan solusi yang terstruktur, objektif, dan efisien bagi pihak sekolah dalam pengambilan keputusan strategis penempatan siswa PKL.
Performance Comparison of Facial Skin Type Classification Using the Segment Anything Model Nisrina Nur Kumala; Asfan Muqtadir; Amaludin Arifia
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.v6i2.2604

Abstract

The facial skin is the first area to often experience various problems. Knowing one’s skin type is an important step in choosing the right skincare routine, but it can be difficult to determine accurately without a specialist's help, which can be costly. To address this, a deep learning approach can be applied to help automatically classify skin types. In this study, several combinations of CNN, MobileNetV3, and SAM models were applied and compared for facial skin type classification. The dataset used, sourced from the figshare platform, consists of 2,250 facial images representing 5 skin types: normal, dry, oily, sensitive, and combination. The dataset was divided into three parts: training (80%), validation (10%), and testing (10%). Each model was evaluated using a confusion matrix, with accuracy, precision, recall, and F1-score metrics used to determine and compare model performance. The results show that the CNN performed worst, while the MobileNetV3-based CNN was the best-performing model, achieving an accuracy of 97%. Meanwhile, adding SAM did not improve performance and actually decreased accuracy. This study demonstrates that using MobileNetV3 without segmentation is more effective than adding SAM segmentation for facial skin type classification.
Konsep Lean Supply Chain dalam Meningkatkan Efisiensi Operasional: The Lean Supply Chain Concept in Improving Operational Efficiency Rian Refanza Lumbantoruan; Jeperson Hutahaean; Parini Parini
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.v6i2.2606

Abstract

Penelitian ini dilatarbelakangi oleh permasalahan pengelolaan persediaan dan rantai pasok pada Toko Leni Love Hijab yang belum berjalan optimal, sehingga sering terjadi kelebihan stok pada beberapa produk dan kekurangan stok pada produk yang memiliki permintaan tinggi. Kondisi ini berdampak pada menurunnya efisiensi operasional serta pelayanan kepada pelanggan. Tujuan penelitian ini adalah untuk menganalisis penerapan konsep Lean Supply Chain dalam meningkatkan efisiensi operasional pada Toko Leni Love Hijab. Metode penelitian yang digunakan adalah metode kualitatif dengan teknik pengumpulan data melalui observasi, wawancara, dan studi dokumentasi. Penelitian ini juga melakukan analisis sistem serta perancangan sistem menggunakan pendekatan Supply Chain Management berbasis teknologi informasi dengan dukungan pemodelan sistem seperti UML dan perancangan basis data. Hasil penelitian menunjukkan bahwa penerapan konsep Lean Supply Chain dapat membantu mengidentifikasi aktivitas yang tidak memberikan nilai tambah, mengoptimalkan pengelolaan persediaan, mempercepat aliran informasi antara toko dan supplier, serta meminimalkan keterlambatan pengiriman produk. Kesimpulan dari penelitian ini adalah bahwa penerapan Lean Supply Chain mampu meningkatkan efisiensi operasional, memperbaiki manajemen stok, serta meningkatkan kualitas pelayanan kepada pelanggan pada Toko Leni Love Hijab

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