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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 41 Documents
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Search results for , issue "Vol. 5 No. 3 (2025): MALCOM July 2025" : 41 Documents clear
Eksplorasi Variabel Berpengaruh dan Akurasi Algoritma Naive Bayes Classifier untuk Mengklasifikasikan Performa Siswa Sekolah Dasar: Exploration of Influential Variables and Accuracy of the Naive Bayes Classifier Algorithm for Classifying the Performance of Elementary School Students Pekuwali, Arini Aha; Bano, Vidriana Oktoviana; Panja, Alfred Domu D.; Prasetyo, Fajar Indra
MALCOM: Indonesian Journal of Machine Learning and Computer Science Vol. 5 No. 3 (2025): MALCOM July 2025
Publisher : Institut Riset dan Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/malcom.v5i3.1813

Abstract

Literasi numerasi memungkinkan seseorang untuk menggunakan angka dan simbol matematika dasar guna menyelesaikan tantangan praktis dalam kehidupan sehari-hari. Data perkembangan kemampuan matematika di antara siswa Indonesia melalui penilaian Program for International Student Assessment (PISA) pada tahun 2022, menunjukkan bahwa Indonesia berada pada posisi ke-71 dari 81 negara. Hasil PISA yang rendah tersebut terkonfirmasi oleh hasil nilai Asesmen Kompetensi Minimum (AKM) literasi numerasi Sumba Timur yang berada di angka 33,37 (skala 0-100) pada tahun 2023. Nilai PISA dan AKM yang sangat rendah menunjukkan rendahnya pondasi kemampuan matematika anak, sehingga perlu adanya pengidentifikasian sejak dini kepada siswa Sekolah Dasar (SD). Perkembangan data dalam konteks pendidikan dan evolusi pendidikan modern telah mendorong penggunaan berbagai teknik data mining untuk memantau performa siswa dengan cara-cara penelusuran yang beragam untuk menganalisis dan menemukan informasi yang tersembunyi dalam sistem pendidikan. Data mining pada data pendidikan biasa disebut dengan educational data mining (EDM). Penelitian dilakukan dengan mengumpulkan data hasil belajar siswa SD kelas 4 untuk mata pelajar matematika dan beberapa data demografis siswa. Melalui penelitian ini diketahui bahwa variabel RT1, RT2, dan PTS memiliki hubungan yang kuat dengan variabel terikat PAS. Model yang dibentuk oleh Algoritme Naive Bayes berhasil mengklasifikasikan performa belajar siswa dengan akurasi sebesar 92%.
Revolutionizing Corporate Event Planning with AI: A Cost-Efficiency Strategy for BuatEvent.id Supriyadi, Muhammad; Rianto, Yan
MALCOM: Indonesian Journal of Machine Learning and Computer Science Vol. 5 No. 3 (2025): MALCOM July 2025
Publisher : Institut Riset dan Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/malcom.v5i3.1929

Abstract

BuatEvent.id leverages an AI-driven platform for event planning, powered by Gemini.ai—a sophisticated NLP model with an accuracy rate of 92.5%. The system integrates multiple technologies, including PHP, Python, Golang, Flutter, and MySQL, to automate essential processes, achieving a 25% improvement in planning precision. This study aims to evaluate the role of AI in enhancing budget management and corporate event customization. By addressing the inefficiencies of conventional event planning, this platform optimizes workflows, enhances overall productivity, and offers a seamless user experience customized to cater to a wide range of client requirements. The results demonstrate a 92.5% accuracy in processing user queries and a 25% increase in event planning efficiency, highlighting the platform’s ability to deliver cost-effective and personalized solutions. These figures were obtained through internal testing using a dataset of 200 annotated user queries. The platform primarily targets corporate events, including workshops, product launches, and business meetings.For example, the system was successfully deployed during a corporate training event in Jakarta, where it reduced planning time by 30%.
AI-Powered: Leveraging Teachable Machine for Real-time Scanner Marcelly, Frizca Fellicita; Rianto, Yan
MALCOM: Indonesian Journal of Machine Learning and Computer Science Vol. 5 No. 3 (2025): MALCOM July 2025
Publisher : Institut Riset dan Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/malcom.v5i3.1931

Abstract

Effective inventory control is essential in optimizing profitability through cost control and efficiency expectations. Conventional inventory techniques frequently find it difficult to adjust to the fast-changing restaurant setting, resulting in surplus stock, inventory deficits, and unnecessary food waste. Nonetheless, a notable shift is approaching, as the incorporation of artificial intelligence (AI) may help address this issue. AI-powered inventory management systems help restaurants optimize stock levels, reduce waste, and predict demand more accurately, leading to improved efficiency and increased profitability. This study explores how AI-driven inventory management enhances efficiency, reduces waste, and automates restocking in the restaurant sector, with a particular focus on TastyGo's integration of Teachable Machine and TensorFlow Lite. The suggested solution uses picture recognition for real-time inventory tracking, and machine learning models to predict demand and replenishment automation. TastyGo can expedite supply chain management, save waste through predictive analytics, and improve its inventory by employing these AI techniques. This study shows how AI-driven solutions may boost decision-making, reduce food waste, and greatly increase operational efficiency, all of which can result in higher profitability. The findings highlight how AI technologies have the potential to revolutionize conventional inventory management systems in the restaurant industry.
Real-Time Road Damage Detection on Mobile Devices using TensorFlow Lite and Teachable Machine Nova, Lusindah; Rianto, Yan
MALCOM: Indonesian Journal of Machine Learning and Computer Science Vol. 5 No. 3 (2025): MALCOM July 2025
Publisher : Institut Riset dan Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/malcom.v5i3.1933

Abstract

This study presents a mobile-based road damage detection system using Teachable Machine and TensorFlow Lite to support real-time monitoring and efficient infrastructure maintenance. The system identifies road damage types such as cracks, potholes, and uneven surfaces. The RDD2020 dataset is used for model training, with preprocessing steps including augmentation, normalization, and resizing. A Convolutional Neural Network (CNN) model is trained through Teachable Machine for ease of customization. TensorFlow Lite is employed for on-device inference, with optimization techniques like quantization and pruning applied to improve speed and reduce model size. The system is evaluated using precision, recall, F1-score, and accuracy metrics under varying lighting and weather conditions. The final model is deployed in a mobile app using TensorFlow Lite Interpreter for efficient performance. Experimental results show high detection accuracy, with a precision of X% and F1-score of Y% (insert actual values). This approach offers a lightweight, cost-effective solution for road maintenance authorities and urban planners. Future enhancements include dataset expansion, integration with mapping tools, and improved robustness in diverse environments. Overall, the proposed system enables real-time, accurate road damage detection and supports smarter, eco-friendly infrastructure management.
Smart Prescription Reader: Enhancing Accuracy in Medical Prescriptions Yulianto, Ragil
MALCOM: Indonesian Journal of Machine Learning and Computer Science Vol. 5 No. 3 (2025): MALCOM July 2025
Publisher : Institut Riset dan Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/malcom.v5i3.1934

Abstract

Reading a doctor's handwritten prescription is a challenge faced by most patients and some pharmacists, which in some cases can lead to negative consequences due to misinterpretation of the prescription. The "Doctor's Handwritten Prescription BD Dataset" on Kaggle contains segmented images of handwritten prescription words from BD (Bangladesh) doctors. This dataset, intended for machine learning applications, includes 4,680 individual words segmented from prescription images. This study introduces a Handwriting Recognition System using Convolutional Neural Network (CNN) developed to identify text in prescription images written by doctors and convert the cursive handwriting into readable text. Two models were evaluated in this study: CNN and MobileNet. Based on the experiments, MobileNet showed better results compared to CNN alone. From the dataset of 4,680 words, 3,120 were used for training, 780 for testing, and 780 for validation. The study achieved a training accuracy of 97%, a testing accuracy of 88%, and a validation accuracy of 83%. The developed model was successfully implemented in a web application
Pengunaan Barcode dalam Sistem Inventory Modern untuk Meningkatkan Akurasi dan Kecepatan Operasional: Utilization of Barcode Technology in Modern Inventory Systems to Enhance Accuracy and Operational Efficiency Maulana, Sahidin Achmad Noor; Wijayanti, Esti; Chamid, Ahmad Abdul
MALCOM: Indonesian Journal of Machine Learning and Computer Science Vol. 5 No. 3 (2025): MALCOM July 2025
Publisher : Institut Riset dan Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/malcom.v5i3.1943

Abstract

Pengelolaan stok di gudang sering menghadapi tantangan seperti kesalahan pencatatan, inefisiensi proses, dan kurangnya transparansi data. Untuk mengatasinya, penelitian ini bertujuan merancang dan mengimplementasikan aplikasi inventory berbasis barcode guna meningkatkan efisiensi dan akurasi operasional. Aplikasi dilengkapi dengan fitur seperti pendaftaran produk, pemindaian barcode untuk barang masuk dan keluar, serta pencatatan riwayat transaksi secara real-time dan terintegrasi. Teknologi barcode memungkinkan pencatatan otomatis yang dapat mengurangi kesalahan manusia dan mempercepat proses pengelolaan stok. Metode pengembangan sistem yang digunakan adalah metode Prototyping, yang memungkinkan pengembangan sistem dilakukan secara bertahap melalui pembuatan model awal dan penyempurnaan berkelanjutan berdasarkan umpan balik pengguna. Pendekatan ini sesuai diterapkan dalam kondisi di mana kebutuhan sistem belum sepenuhnya terdefinisi sejak awal. Studi kasus dilakukan pada sebuah gudang distribusi untuk menguji potensi aplikasi dalam meningkatkan keandalan data dan transparansi pelaporan. Penelitian ini diharapkan dapat memberikan kontribusi terhadap pengembangan sistem inventory yang lebih adaptif, akurat, dan efisien di sektor logistik dan distribusi.
Internet of Things Based Air Quality Monitoring System with Automatic Notification Azizah, Devi Nur; Heranurweni, Sri; Idris, La Ode Muhamad
MALCOM: Indonesian Journal of Machine Learning and Computer Science Vol. 5 No. 3 (2025): MALCOM July 2025
Publisher : Institut Riset dan Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/malcom.v5i3.1945

Abstract

Internet of Things (IoT)-based air quality monitoring systems represent a significant advancement in urban environmental management. This research implements a system that integrates PM2.5, PM10, CO2, and NO2 sensors for real-time monitoring of pollutants. The results showed that the integration of IoT technology with cloud computing and machine learning algorithms successfully created a responsive and accurate monitoring system. The model achieved maximum accuracy during the training process, with promising predictive capabilities in real-world implementation. The main findings of the study confirmed that the Weighted Class (WC) approach significantly improved performance in the testing and prediction process by addressing class imbalance in the dataset, while the Data Augmentation (DA) technique did not show the expected improvement due to the intrinsic characteristics of air quality data. The automatic notification system successfully provides early warnings when air quality exceeds specified thresholds, enabling proactive responses from authorities and the public. The implementation of a web-based monitoring dashboard provides comprehensive visualization of data for long-term analysis. This research contributes to the development of smart cities by providing an effective framework for air quality management, supporting data-driven decision-making, and increasing public awareness of environmental conditions.
Perbandingan Kinerja Algoritma Clustering K-Means dan K-Medoids dalam Pengelompokan Sekolah di Provinsi Riau Berdasarkan Ketersediaan Sarana dan Prasarana: Comparison of K-Means and K-Medoids Clustering Algorithm Performance in Grouping Schools in Riau Province Based on Availability of Facilities and Infrastructure Salman, Muhammad Dzaki; Rahmaddeni, Rahmaddeni; Pratama , Nanda Rizki; A, M. Nakhlah Farid; Setiawan, Ahmad Agung; Zalianti, Fenisya; Huda, Isra Bil
MALCOM: Indonesian Journal of Machine Learning and Computer Science Vol. 5 No. 3 (2025): MALCOM July 2025
Publisher : Institut Riset dan Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/malcom.v5i3.1950

Abstract

Pendidikan yang berkualitas sangat dipengaruhi oleh ketersediaan sarana dan prasarana yang memadai. Penelitian ini bertujuan untuk membandingkan kinerja dua algoritma clustering, yaitu K-Means dan K-Medoids, dalam mengelompokkan 497 sekolah negeri di Provinsi Riau yang terdiri dari jenjang SD, SMP, SMA, dan SMK. Data yang dianalisis meliputi jumlah guru, siswa, ruang kelas, laboratorium, akses internet, sanitasi, dan status akreditasi. Data diperoleh dari Dinas Pendidikan dan Badan Pusat Statistik (BPS) Provinsi Riau, kemudian dianalisis melalui Exploratory Data Analysis (EDA), preprocessing, dan reduksi dimensi dengan Principal Component Analysis (PCA). Hasil evaluasi menggunakan Davies-Bouldin Index (DBI) dengan k=3 menunjukkan bahwa K-Medoids menghasilkan cluster yang lebih terpisah dan lebih baik (0,61) dibandingkan K-Means (0,80). Keunggulan K-Medoids terletak pada ketahanannya terhadap outlier dan distribusi data yang tidak merata. Hasil penelitian ini dapat digunakan sebagai acuan dalam perencanaan kebijakan pendidikan yang lebih merata dan tepat sasaran di Provinsi Riau.
Perbandingan Algoritma K-Nearest Neighbors dan Random Forest untuk Rekomendasi Gaya Hidup Sehat dalam Mencegah Penyakit Jantung: Comparison of K-Nearest Neighbors and Random Forest Algorithms for Recommendations for a Healthy Lifestyle in Prevent Heart Disease Sahelvi, Elza; Cikita, Putri; Sapitri, Riska Mela; Rahmaddeni, Rahmaddeni; Efrizoni, Lusiana
MALCOM: Indonesian Journal of Machine Learning and Computer Science Vol. 5 No. 3 (2025): MALCOM July 2025
Publisher : Institut Riset dan Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/malcom.v5i3.1972

Abstract

Penyakit jantung merupakan salah satu penyebab utama kematian yang disebabkan oleh faktor gaya hidup tidak sehat. Untuk mengatasi permasalahan ini, penelitian ini membandingkan algoritma K-Nearest Neighbors (KNN) dan Random Forest (RF) dalam memberikan rekomendasi gaya hidup sehat guna mencegah penyakit jantung. Dataset yang digunakan terdiri dari 1.025 entri dengan 14 fitur, yang telah melalui tahap preprocessing, termasuk normalisasi, seleksi fitur, dan pembagian data 80:20 serta 70:30. Evaluasi model dilakukan menggunakan metrik akurasi, presisi, recall, dan F1-score. Hasil penelitian menunjukkan bahwa Random Forest memiliki akurasi lebih tinggi (99% pada skenario 80:20 dan 98% pada skenario 70:30) dibandingkan KNN (83% dan 86%), serta lebih stabil dalam mengklasifikasikan risiko penyakit jantung. Analisis fitur menunjukkan bahwa Chest Pain Type (CP) atau nyeri dada merupakan faktor paling berpengaruh. Berdasarkan hasil ini, direkomendasikan pola makan sehat, aktivitas fisik teratur, manajemen stres, serta pemeriksaan kesehatan rutin. Kesimpulannya, Random Forest lebih efektif dalam sistem rekomendasi gaya hidup sehat, dan penelitian selanjutnya dapat menggunakan dataset lebih besar dengan variabel tambahan guna meningkatkan akurasi prediksi.
Model Prediksi Dampak Perubahan Iklim pada Ketahanan Pangan Menggunakan Algoritma Support Vector Machine and K-Nearest Neighbors: Prediction Model for the Impact of Climate Change on Food Security Using the Support Vector Machine and K-Nearest Neighbors Algorithms Sari, Devi Puspita; Risman, Risman; Maulana, Fitra; Efrizoni, Lusiana; Rahmaddeni, Rahmaddeni
MALCOM: Indonesian Journal of Machine Learning and Computer Science Vol. 5 No. 3 (2025): MALCOM July 2025
Publisher : Institut Riset dan Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/malcom.v5i3.1975

Abstract

Perubahan iklim memberikan dampak signifikan terhadap ketahanan pangan global, terutama di wilayah yang sangat bergantung pada sektor agrikultur. Fenomena seperti curah hujan ekstrem, kenaikan suhu, dan perubahan pola angin telah memengaruhi produktivitas pertanian secara signifikan. Urgensi penelitian ini terletak pada pentingnya pengembangan model prediktif berbasis data untuk mengantisipasi dampak perubahan iklim terhadap ketahanan pangan, sehingga strategi adaptasi dapat dirancang secara tepat oleh pembuat kebijakan. Penelitian ini bertujuan mengembangkan model prediksi dampak perubahan iklim terhadap ketahanan pangan dengan memanfaatkan algoritma Support Vector Machine (SVM) dan K-Nearest Neighbors (KNN). Dataset yang digunakan meliputi data meteorologi harian, seperti curah hujan (precipitation), suhu maksimum (temp_max), suhu minimum (temp_min), dan kecepatan angin (wind), yang diperoleh dari Kaggle (Seattle weather). Model SVM diterapkan untuk menangkap hubungan non-linear antara parameter iklim dengan indikator ketahanan pangan, sedangkan KNN digunakan untuk menganalisis pola serupa pada data historis. Hasil penelitian menunjukkan bahwa SVM memiliki akurasi prediksi sebesar 78%, lebih unggul dibandingkan KNN yang mencapai akurasi 74%. Temuan ini membuktikan bahwa SVM lebih efektif dalam memodelkan keterkaitan antara variabel iklim dan ketahanan pangan. Dengan demikian, penelitian ini berhasil mencapai tujuannya dan memberikan kontribusi penting dalam pengembangan sistem prediksi berbasis machine learning untuk mendukung kebijakan pangan yang adaptif terhadap perubahan iklim.

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