cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
Mustakim
Contact Email
officialmalcom.irpi@gmail.com
Phone
+6285275359942
Journal Mail Official
malcom@irpi.or.id
Editorial Address
INSTITUT RISET DAN PUBLIKASI INDONESIA Jl. Tuah Karya Ujung C7. Kel. Tuah Madani Kec. Tampan Kota Pekanbaru - Riau
Location
Kota pekanbaru,
Riau
INDONESIA
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 418 Documents
 32   33   34   35   36 
Impact of Cover Parameter Value on Rule Generation in Rough Set Classification Fityah, Farhatul; Sofyan , Pramudya Rakhmadyansyah
MALCOM: Indonesian Journal of Machine Learning and Computer Science Vol. 5 No. 2 (2025): MALCOM April 2025
Publisher : Institut Riset dan Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/malcom.v5i2.1831

Abstract

Machine learning plays a crucial role in healthcare classification, with Rough Set Theory (RST) offering effective tools for managing data uncertainty. Within RST, the RSES2 tool supports algorithms like LEM2 and Covering, yet the influence of cover parameter values on rule generalization and specificity remains underexplored. This study investigates these effects using the Differentiated Thyroid Cancer dataset. The research investigates the trade-offs between rule generalization and specificity by adjusting cover parameter settings, which dictate the minimum and maximum cases a rule must cover. The comparison reveals that the LEM2 algorithm maintains high accuracy across various cover parameter values, with only a slight decline as the parameter increases, and shows improved coverage with higher cover values. In contrast, the Covering algorithm displays greater fluctuations in accuracy, peaking at lower cover parameter values and decreasing significantly as the parameter rises. Coverage for the Covering algorithm is highest at lower cover parameters but decreases sharply at higher values. This indicates that LEM2 is more robust in maintaining accuracy and coverage, while the Covering algorithm performs better at lower cover parameters but struggles with stability as the parameter increases.
Analisis Minat Belanja Mahasiswa Universitas Internasional Batam Selama Live Streaming dengan Pendekatan Model Technology Acceptance Model: Analysis of Shopping Interest of Batam International University Students During Live Streaming with Technology Acceptance Model Approach Yuliana, Yuliana; Simanjuntak, Fredian; Pratama, Jimmy
MALCOM: Indonesian Journal of Machine Learning and Computer Science Vol. 5 No. 2 (2025): MALCOM April 2025
Publisher : Institut Riset dan Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/malcom.v5i2.1847

Abstract

Dalam era digital, live streaming telah menjadi salah satu strategi pemasaran yang berkembang pesat, terutama di platform e-commerce seperti Shopee Live dan TikTok Live di Universitas Internasional Batam (UIB). Penelitian ini bertujuan untuk menganalisis minat belanja mahasiswa Universitas Internasional Batam selama live streaming, dengan menggunakan pendekatan Model Technology Acceptance Model (TAM). Mahasiswa merupakan segmen konsumen yang unik karena mereka memiliki karakteristik berbeda dari kelompok usia lainnya, seperti keterbatasan anggaran, kebiasaan belanja yang berbasis digital, serta kepercayaan terhadap platform online. Oleh karena itu, penting untuk memahami faktor-faktor yang memengaruhi minat belanja mereka saat live streaming. Hal ini cukup penting terutama di Universitas Internasional Batam karena mahasiswa di Universitas Internasional Batam mayoritas sangat mampu, akan tetapi lebih memilih untuk menyisihkan pendapatannya untuk membayar keperluan apa adanya sehingga minat belanjanya cenderung rendah. Penelitian ini menggunakan pendekatan kuantitatif dengan metode survei dan akan dianalisis dengan regresi linear untuk menguji hubungan antar variabel. Hasil penelitian menunjukkan bahwa kegunaan dan kemudahan penggunaan berpengaruh signifikan terhadap sikap terhadap e-commerce. Selain itu, privasi ditemukan berpengaruh terhadap keamanan. Penelitian ini juga memberikan rekomendasi bagi Usaha Kecil Menengah (UKM) untuk mengoptimalkan strategi pemasaran dengan memanfaatkan live streaming secara lebih efektif, khususnya dalam meningkatkan keterlibatan dan pengalaman konsumen.
Analisis Aplikasi E-Commerce pada Generasi Z dengan Pendekatan System Usability Scale: Analysis of E-Commerce Applications in Generation Z with the System Usability Scale Approach Lim, Julianto Wijaya Akoi; Deli, Deli; Adnas, Diny Anggraini
MALCOM: Indonesian Journal of Machine Learning and Computer Science Vol. 5 No. 2 (2025): MALCOM April 2025
Publisher : Institut Riset dan Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/malcom.v5i2.1851

Abstract

Kota Batam sebagai kota industri dan perdagangan memiliki pertumbuhan e-commerce yang pesat, tetapi belum ada studi spesifik yang menganalisis usability aplikasi e-commerce pada Generasi Z di wilayah ini. Oleh karena itu, penelitian ini penting untuk mengidentifikasi faktor usability yang dapat meningkatkan pengalaman pengguna dan memberikan wawasan bagi pengembang aplikasi. Penelitian ini bertujuan untuk mengukur tingkat kepuasan dan efektivitas aplikasi serta hambatan yang terjadi dengan pendekatan System Usability Scale (SUS). Penelitian ini menggunakan metode kuantitatif dengan kuesioner berbasis System Usability Scale (SUS) dengan generasi Z di Kota Batam sebagai populasi penelitian, dan cluster disproportionate random sampling sebagai metode sampling. Penelitian ini membandingkan kegunaan tiga aplikasi e-commerce; Shopee, Tokopedia, dan Lazada. Hasil penelitian menunjukkan nilai usability ketiga aplikasi e-commerce berbeda tipis antara Shopee (90,06), Tokopedia (91,98), dan Lazada (88,42). Berdasarkan jenis kelamin, perempuan lebih memilih Shopee, sedangkan laki-laki lebih memilih Tokopedia dan Lazada. Dari segi usia, rentang 20–24 tahun lebih dominan dibanding 15–19 tahun, sementara berdasarkan pekerjaan, mahasiswa/pelajar cenderung menggunakan Lazada, dan pegawai swasta lebih memilih Shopee dan Tokopedia. Shopee dan Tokopedia berada pada performa optimal, sedangkan Lazada memiliki potensi pengembangan, terutama untuk pegawai swasta dan pengguna usia 15–19 tahun.
Penerapan Algoritma Naive Bayes dengan Teknik TF-IDF dan Cross Validation untuk Analisis Sentimen Terhadap Starlink: Application of the Naive Bayes Algorithm with TF-IDF and Cross Validation Techniques for Sentiment Analysis Towards Starlink Khoerunnisa, Suci; Shiddieq, Diqy Fakhrun; Nurhayati, Dwi
MALCOM: Indonesian Journal of Machine Learning and Computer Science Vol. 5 No. 2 (2025): MALCOM April 2025
Publisher : Institut Riset dan Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/malcom.v5i2.1852

Abstract

Starlink, layanan internet satelit dari SpaceX, mulai beroperasidi Indonesia pada 2024 untuk mengatasi kesenjangan digital di wilayah terpencil. Namun, kehadirannya menimbulkan tantangan seperti harga tinggi, potensi dampak terhadap penyedia lokal, dan masalah regulasi. Penelitian ini mengkaji sentimen publik terhadap Starlink menggunakan algoritma Naïve Bayes yang dikombinasikan dengan teknik TF-IDF dan Cross Validation yang masih jarang diterapkan dalam studi serupa di Indonesia. Data yang digunakan berupa cuitan berbahasa Indonesia dari pengguna platform X selama Mei-November 2024. Hasil analisis menunjukkan bahwa model Naïve Bayes memiliki kinerja optimal dalam mendeteksi sentimen positif dibandingkan negatif maupun netral, sebagaimana diukur menggunakan confusion matrix. Temuan utama menunjukkan bahwa Naïve Bayes 49,38% cuitan bersentimen positif, 32,94% netral, dan 17,68% negatif. Sentimen positif didominasi oleh apresiasi terhadap kecepatan dan stabilitas layanan, sedangkan sentimen negatif mengkritik harga tinggi dan dampaknya terhadap penyedia lokal. Meskipun model menunjukkan performa baik pada sentimen positif, akurasi klasifikasi sentimen negatif dan netral masih perlu ditingkatkan. Hasil penelitian ini memberikan wawasan strategis bagi pengembangan bisnis Starlink serta dasar pertimbangan bagi pemerintah terkait layanan internet berbasis satelit di Indonesia.
Analysis of Employee Capacity Gap in Managing Network Security and Its Implementation Towards Insider Threat Prevention Sitorus, Felix Noel; Harwahyu, Ruki
MALCOM: Indonesian Journal of Machine Learning and Computer Science Vol. 5 No. 2 (2025): MALCOM April 2025
Publisher : Institut Riset dan Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/malcom.v5i2.1878

Abstract

Network security is crucial for protecting organizational information in the rapidly evolving digital era. Threats to networks do not only come from external sources, such as malware or hacking, but also from within the organization, known as insider threats. These threats can cause significant losses, whether due to intentional or unintentional actions by employees or internal parties with access to the system. Therefore, employees' ability to manage network security is key to addressing these threats. Handling insider threats must be a top priority for organizations. This study aims to analyze the employee capacity gap in managing network security and its impact on preventing insider threats in XYZ Organization. By implementing ISO 27001 security standards, particularly within the context of the Information Security Management System (ISMS) using the PDCA approach, this research evaluates how human resource management relates to information asset management and network security maintenance. The findings indicate that gaps in employees' knowledge and skills regarding network security significantly contribute to vulnerabilities against insider threats. This study also highlights how the implementation of ISO 27001, which emphasizes asset analysis and the PDCA cycle, can help organizations improve information security governance and prevent insider threats
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.

Page 34 of 42 | Total Record : 418

 32   33   34   35   36 

Filter by Year

2021 2025


Reset
Filter By Issues
All Issue Vol. 5 No. 4 (2025): MALCOM October 2025 Vol. 5 No. 3 (2025): MALCOM July 2025 Vol. 5 No. 2 (2025): MALCOM April 2025 Vol. 5 No. 1 (2025): MALCOM January 2025 Vol. 4 No. 4 (2024): MALCOM October 2024 Vol. 4 No. 3 (2024): MALCOM July 2024 Vol. 4 No. 2 (2024): MALCOM April 2024 Vol. 4 No. 1 (2024): MALCOM January 2024 Vol. 3 No. 2 (2023): MALCOM October 2023 Vol. 3 No. 1 (2023): MALCOM April 2023 Vol. 2 No. 2 (2022): MALCOM October 2022 Vol. 2 No. 1 (2022): MALCOM April 2022 Vol. 1 No. 2 (2021): MALCOM October 2021 Vol. 1 No. 1 (2021): MALCOM April 2021 More Issue