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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 418 Documents
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Implementasi Algoritma Support Vector Machine Untuk Analisa Sentimen Data Ulasan Aplikasi Pinjaman Online di Google Play Store: Implementation of Support Vector Machine Algorithm for Sentiment Analysis of Online Loan Application Review Data on Google Play Store Iqbal, Muhammad; Afdal, M; Novita, Rice
MALCOM: Indonesian Journal of Machine Learning and Computer Science Vol. 4 No. 4 (2024): MALCOM October 2024
Publisher : Institut Riset dan Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/malcom.v4i4.1435

Abstract

Pinjaman online (pinjol) banyak menuai pro dan kontra karena aksesnya yang mudah dan iklannya tersebar di media sosial. Penyelenggara pinjaman daring juga seringkali menggunakan metode penagihan yang mengganggu, memberlakukan bunga yang tinggi, dan menetapkan jangka waktu pembayaran yang pendek, terutama pada pinjaman daring ilegal. Karenanya, penelitian ini melakukan analisis sentimen pada lima aplikasi pinjol, yaitu Kredivo, Easycash, Rupiah Cepat, Kredit Pintar, dan Ada Pundi. Data ulasan aplikasi diambil dari Google Play Store menggunakan teknik scraping. Kemudian, pelabelan sentimen dilakukan secara otomatis menggunakan kamus sentimen Bahasa Indonesia (Inset). Hasil pelabelan menunjukkan bahwa semua aplikasi pinjol mayoritas memiliki sentimen negatif. Kredivo menjadi aplikasi dengan jumlah sentimen positif terbanyak (46%), sementara itu Easycash memiliki sentimen negatif terbanyak (65%). Data yang di labeli kemudian digunakan untuk pemodelan klasifikasi dengan algoritma Support Vector Machine (SVM). Hasil evaluasi menghasilkan algoritma SVM mempunyai kinerja yang cukup baik dengan rata-rata akurasi sebesar 72%, presisi 76%, dan recall 85%. Namun secara khusus, SVM sangat baik melakukan klasifikasi sentimen pada aplikasi Kredit Pintar dengan akurasi sebesar 83%. Analisis visualisasi menggunakan word cloud juga dilakukan untuk memahami konteks ulasan pengguna aplikasi pinjol. Hasil pengamatan menunjukkan bahwa pengguna hampir selalu membahas tentang limit pinjaman disetiap sentimen pada kelima aplikasi.
Analysis of Measuring Information Security Awareness for Employees at Institution XYZ Permadi, Rachmat Bayu; Ramli, Kalamullah
MALCOM: Indonesian Journal of Machine Learning and Computer Science Vol. 4 No. 4 (2024): MALCOM October 2024
Publisher : Institut Riset dan Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/malcom.v4i4.1453

Abstract

As a government institution in the field of civil servant management, XYZ Institution has data on 4.4 million Employees spread throughout Indonesia which needs to be maintained.  Based on the BSSN report, there has been a significant increase in potential threats in recent years and is expected to continue in 2024, one of which is the threat of Phishing. This research was conducted to measure the level of information security awareness (ISA) for employees at xyz institution. Phishing simulations and questionnaires are used to measure the level of ISA and how to provide ISA education so that it can increase the level of  ISA employees. Simulation results will be compared before and after the provision of ISA education. The results of providing education have a positive impact for employees. Simulation before providing education there were 65% of employees clicking on phishing urls and after education there was a decrease to 17%. While employees who were exposed to phishing before education were 33% and after education there was a decrease to 16%. In addition, the questionnaire filled out by 150 employees showed results with a value of 86.54%  for the level of ISA employee, which is in the good category
Comparison of Sentiment Analysis Algorithms with SMOTE Oversampling and TF-IDF Implementation on Google Reviews for Public Health Centers Budaya, I Gede Bintang Arya; Suniantara, I Ketut Putu
MALCOM: Indonesian Journal of Machine Learning and Computer Science Vol. 4 No. 3 (2024): MALCOM July 2024
Publisher : Institut Riset dan Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/malcom.v4i3.1459

Abstract

Sentiment analysis, or opinion mining, is a key area of natural language processing that identifies sentiments in free text. As digital business services grow and user-generated content increases, analyzing sentiments in online reviews is vital for enhancing business operations and customer satisfaction. This study focuses on sentiment analysis of user reviews from Google Reviews for Public Health Centers (PHCs) in Bali, Indonesia, using five machine learning models: Logistic Regression, Support Vector Machine (SVM), XGBoost, Naive Bayes, and Random Forest. These models classified sentiments into positive and negative categories using a dataset balanced with SMOTE to improve accuracy. We divided a total of 1.834 reviews, using 20% for testing and 80% for training, to ensure a thorough evaluation under real-world conditions. Logistic Regression and Naive Bayes performed best, both achieving an accuracy of 0.89, with Logistic Regression providing a balanced precision and recall. The study enhances academic understanding of sentiment analysis in healthcare and offers insights for business administrators on handling online customer feedback. The findings stress the importance of choosing suitable machine learning techniques based on specific data characteristics and project requirements to optimize both technological and business outcomes.
Measuring Information Security Awareness Level of High School Students Perkasa, Dimas Agung; Setiawan, Bambang
MALCOM: Indonesian Journal of Machine Learning and Computer Science Vol. 4 No. 4 (2024): MALCOM October 2024
Publisher : Institut Riset dan Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/malcom.v4i4.1461

Abstract

Information security awareness has become a crucial factor in the current advancement of information technology. Information security awareness is one of the key factors in avoiding crimes in the digital world today. Therefore, this research aims to measure the information security awareness level and provide recommendations to enhance the information security awareness of high school students. The research instrument utilized in this study is the Human Aspect Information Security Questionnaire (HAIS-Q) and focus area weighting was conducted using the Analytic Hierarchy Process (AHP) method. Data was collected through questionnaires distributed to 99 respondents, and the weighting was performed by two experts in the field of information security and one high school teacher. The results indicated a total awareness level of 86,38%, categorized as "Good", with the most vulnerable focus area being password management. Based on these findings, recommendations are provided in this research to enhance information security awareness.
Comparison of the Accuracy of The Bahasa Isyarat Indonesia (BISINDO) Detection System Using CNN and RNN Algorithm for Implementation on Android Aryananda, I Gusti Agung Oka; Samopa, Febriliyan
MALCOM: Indonesian Journal of Machine Learning and Computer Science Vol. 4 No. 3 (2024): MALCOM July 2024
Publisher : Institut Riset dan Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/malcom.v4i3.1465

Abstract

Communication is a process of exchanging information that aims to establish relationships between humans. Communication difficulties are an obstacle for people with deaf disabilities or often called Deaf Friends, where they find it difficult to interact with friends around them. Sign language is the main medium of communication used worldwide by people with disabilities i.e. deaf and speech impaired. Communication between deaf people and those around them is often an obstacle because most people do not understand sign language which is often used as a medium of communication by deaf people. In dealing with this problem, researchers want to analyze the accuracy level of the Android-Based Bahasa Isyarat Indonesia Detection System (BISINDO) using the Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN) methods in order to determine which methods can be implemented to an Android device. This study shows that Convolutional Neural Network (CNN) has a greater and more stable accuracy rate compared to the Recurrent Neural Network (RNN) model where the CNN model produces an accuracy rate of 89%, and indicates that the ability to recognize images based on the division of Bahasa Isyarat Indonesia (BISINDO) alphabetic classes is good..
Design and Analysis of Cybersecurity Information Sharing Mechanism Between Computer Security Incident Response Teams (CSIRT) in Indonesia on Blockchain Technology Through Hyperledger Composer and Interplanetary File System (IPFS) Hariyanto, Fajar; Ramli, Kalamullah
MALCOM: Indonesian Journal of Machine Learning and Computer Science Vol. 4 No. 4 (2024): MALCOM October 2024
Publisher : Institut Riset dan Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/malcom.v4i4.1466

Abstract

Sharing cybersecurity information among the Computer Security Incident Response Team (CSIRT) is a crucial step in enhancing organizational cybersecurity. However, a primary challenge faced is the lack of trust among users regarding the confidentiality, integrity, and availability of shared information. This study proposes a new approach by designing a mechanism for sharing cybersecurity information among CSIRTs in Indonesia on blockchain technology using Hyperledger Composer. This approach offers an innovative solution by leveraging the advantages of blockchain technology. Through this approach, cybersecurity information can be shared in a decentralized manner, overcoming the weaknesses of centralized systems, and enhancing overall information security. Another advantage of blockchain technology is its high performance and scalability, enabling increased speed, and user capacity in the process of sharing information. By implementing a blockchain-based mechanism for sharing cybersecurity information, this research aims to ensure crucial aspects of information security, namely confidentiality, integrity, and availability. The contribution of this study is not only in enhancing organizational cybersecurity but also in providing an innovative solution to practical challenges in sharing cybersecurity information among CSIRTs.
Analisis Sentimen Penggunaan TikTok Sebagai Media Pembelajaran Menggunakan Algoritma Naïve Bayes Classifier: Sentiment Analysis of Using TikTok as a Learning Media Using the Naïve Bayes Classifiers Algorithm Apriani, Elsa; Oktavianalisti, Falah; Monasari, La Denna Hasri; Winarni, Indah; Hanif, Isa Faqihuddin
MALCOM: Indonesian Journal of Machine Learning and Computer Science Vol. 4 No. 3 (2024): MALCOM July 2024
Publisher : Institut Riset dan Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/malcom.v4i3.1482

Abstract

Media sosial seperti TikTok telah menjadi platform yang populer di kalangan pengguna internet, terutama di kalangan generasi muda. Di sisi lain, media sosial juga memiliki potensi sebagai media pembelajaran yang efektif. Penelitian ini bertujuan untuk menganalisis sentimen pengguna terkait penggunaan TikTok sebagai media pembelajaran yang menggunakan algoritma Naïve  Bayes untuk menganalisis data sentimen tersebut. Sebanyak 176 data dikumpulkan melalui kuesioner yang disebarkan menggunakan Google Form. Data yang diperoleh kemudian dianalisis menggunakan algoritma Naïve  Bayes Classifiers untuk mengkategorikan sentimen positif, negatif, dan netral. Hasil analisis menunjukkan bahwa akurasi model adalah 52.27%, dengan nilai precision sebesar 19.60%, dan recall sebesar 50.83%. Meskipun akurasi dan precision yang diperoleh relatif rendah, hasil ini memberikan gambaran awal mengenai persepsi pengguna terhadap TikTok sebagai alat pembelajaran. Penelitian ini diharapkan dapat memberikan wawasan lebih lanjut bagi pengembang aplikasi pembelajaran berbasis media sosial dan membantu dalam meningkatkan kualitas konten edukatif di TikTok.
Analisis Kepuasan SI-BTM Menggunakan Metode Technology Acceptance Model (TAM): SI-BTM Satisfaction Analysis Using the Technology Acceptance Model (TAM) Method Medina, Aulia; Tatuhey, Emy L.; Kiswanto, Rahmat H.
MALCOM: Indonesian Journal of Machine Learning and Computer Science Vol. 4 No. 4 (2024): MALCOM October 2024
Publisher : Institut Riset dan Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/malcom.v4i4.1485

Abstract

Dalam upaya meningkatkan efisiensi dan transparansi proses pembayaran tunjangan dan remunerasi bagi Aparatur Sipil Negara (ASN) dan Pegawai Pemerintah dengan Perjanjian Kerja (PPPK), Badan Kepegawaian Pendidikan dan Pelatihan (BKPP) Kota Jayapura telah menerapkan Sistem Informasi Pembayaran Tunjangan dan Remunerasi Berbasis Kinerja (SI-BTM). SI-BTM merupakan aplikasi yang ditujukan bagi berbagai kelompok fungsional di BKPP dan didasarkan pada kinerja pegawai. Penelitian ini bertujuan untuk menganalisis kepuasan pengguna terhadap SI-BTM dengan menggunakan model Technology Acceptance Model (TAM). Model TAM mencakup empat dimensi utama, yaitu persepsi kemudahan penggunaan, persepsi manfaat, niat penggunaan, dan penggunaan aktual. Populasi penelitian ini adalah seluruh ASN dan PPPK di BKPP Kota Jayapura yang berjumlah 1.118 orang. Sampel penelitian diambil secara acak sederhana sebanyak 152 orang melalui kuesioner online. Validitas dan reliabilitas instrumen diuji dengan menggunakan metode Cronbach's Alpha dengan bantuan perangkat lunak SPSS dan Microsoft Excel. Hasil wawancara menunjukkan adanya kendala dalam integrasi fungsi aktivitas antara guru dan tenaga fungsional umum, sehingga menyulitkan proses verifikasi oleh BKPP. Survei menunjukkan bahwa persepsi kemudahan penggunaan dan manfaat SI-BTM memiliki pengaruh signifikan terhadap kepuasan pengguna. Temuan ini menunjukkan bahwa penerapan SI-BTM telah memenuhi sebagian besar harapan pengguna, meskipun masih terdapat tantangan dalam integrasi fungsi aktivitas antar kelompok pengguna.
Predictive Model Comparison for Predicting Condom Use: Comparison of Conventional Logistic Regression and Other Machine Learning Murti, Fadhaa Aditya Kautsar
MALCOM: Indonesian Journal of Machine Learning and Computer Science Vol. 4 No. 4 (2024): MALCOM October 2024
Publisher : Institut Riset dan Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/malcom.v4i4.1489

Abstract

Condom use at first sex remains an important issue as it shapes future sexual behavior. This study aimed to deploy and predict condom use using five different machine learning classification models. Dataset used for this study was from Indonesian Demographic and Health Survey (IDHS) 2017 with a population of interest was male adolescents. We evaluated five different models, namely logistic regression, naïve bayes, K-Nearest Neighbors, support vector machines, and decision tree. Performances of each model were assessed using metrics such as accuracy, specificity, sensitivity, ROC Curve, and AUC Score. Study found that different models exhibit different accuracy, specificity, sensitivity, ROC Curve, and AUC Score. The decision tree and naïve bayes models remained the models with the highest specificity and sensitivity, however the KNN model expressed the highest AUC score. Result from the conventional logistic regression also explained that condom use was associated with education level, age at first sex, and attitude towards condom use. The government is advised to create equal education opportunities for every adolescent and shape better knowledge and condom attitudes. Future studies are advised to enhance the performance of machine learning models using hyperparameter tuning and other methods.
Integrasi Sensor DHT11 dan PIR dalam Sistem Otomatisasi Suhu dan Deteksi Gerakan dalam Ruangan Menggunakan Mikrokontroler Arduino Nano: Integration of DHT11 and PIR Sensors in Indoor Temperature Automation and Motion Detection System Using Arduino Nano Microcontroller Pratifi, Via Khusnul; Sasongko, Ananto Tri; Afandi, Dedi
MALCOM: Indonesian Journal of Machine Learning and Computer Science Vol. 4 No. 3 (2024): MALCOM July 2024
Publisher : Institut Riset dan Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/malcom.v4i3.1490

Abstract

Penelitian ini bertujuan untuk merancang dan membangun sistem kontrol suhu dan deteksi gerakan pada ruangan dengan memanfaatkan Arduino Nano yang dilengkapi dengan sensor DHT11 dan sensor PIR. Jenis penelitian yang digunakan adalah penelitian observatif eksperimental. Penelitian observatif eksperimental dimulai dengan menentukan alat dan bahan yang diperlukan, termasuk perangkat lunak seperti Arduino IDE, Fritzing, Draw.io, dan Microsoft Word. Perangkat keras yang digunakan meliputi laptop HP dengan prosesor AMD Ryzen 5 5500U, Arduino Nano, sensor PIR, sensor DHT11, komponen relay, LCD, Buzzer, LED, power supply 12 volt, kabel, dan kipas angin Maspion F-15 DA. Alat pendukung seperti cutter, obeng, solder, tang potong, dan bor listrik digunakan untuk perakitan komponen elektronika agar sistem dapat berfungsi sesuai rencana. Metode pengumpulan data dalam penelitian ini meliputi observasi langsung terhadap kondisi ruang kelas di SMA N 1 Petanahan, serta studi pustaka untuk mendapatkan landasan teori dan informasi pendukung tentang sistem kontrol suhu, sensor, dan komponen yang digunakan. Hasil penelitian menunjukan bahwa sistem kontrol suhu dan deteksi gerakan telah melewati pengujian yang memuaskan. Sistem mampu menjaga suhu ruangan pada rentang yang diinginkan dengan efektif, sementara sensor DHT11 menunjukkan akurasi yang tinggi dalam pengukuran suhu, dengan perbedaan yang sangat kecil antara nilai yang terbaca dan suhu aktual ruangan. 

Page 23 of 42 | Total Record : 418

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