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All Journal JITK (Jurnal Ilmu Pengetahuan dan Komputer) Journal Information System Development DEVICE Jurnal Minfo Polgan (JMP) Jurnal Sains dan Teknologi Jurnal Pengabdian Masyarakat Bidang Sains dan Teknologi Jurnal Bersama Pengabdian Kepada Masyarakat
Romindo Romindo
Universitas Pelita Harapan
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Pengembangan Sistem Informasi Penyewaan Kendaraan pada CV. Bita Jaya Mandiri Jefri Junifer Pangaribuan; Jusin Jusin; Ade Maulana; Romindo Romindo; Muhammad Thafa Kurniawan
ABDIKAN: Jurnal Pengabdian Masyarakat Bidang Sains dan Teknologi Vol. 1 No. 4 (2022): November 2022
Publisher : Yayasan Literasi Sains Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55123/abdikan.v1i4.1245

Abstract

Car rental is a four-wheeled vehicle rental business. CV Bita Jaya Mandiri is a company engaged in car rental services, auto service and body repair. In the process, the problems that occur are the loss of customer data because they are still using semi-computerization, notes made for payments still use written notes and promotions that cannot be carried out widely. Based on these problems the author aims to create a system that can simplify the rental process and provide rental information online. The method used for the car rental information system is an object-oriented approach. In the application of this system development stage, the prototype method is used, the tools used to create the system are Use case diagrams, use case scenarios, activity diagrams, class diagrams and sequence diagrams. The data collection techniques were carried out by means of observation and interviews. With this information system is expected to facilitate the process of leasing and data processing at this company.
IMPLEMENTASI METODE SAW TERHADAP PEMILIHAN KARYAWAN TERBAIK Rimmar Siringoringo; Romindo Romindo; Rudolfo Rizki Damanik
Journal Information System Development (ISD) Vol 8 No 1 (2023): Journal Information System Development
Publisher : UNIVERSITAS PELITA HARAPAN

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.19166/isd.v8i1.589

Abstract

The best and qualified employees will make the company increase in its operations and can grow rapidly. But the constraints that exist in the CV. Maju Jaya Award for the best employee is still not optimal in its implementation. Therefore we need a system that can assist companies in evaluating the best employees optimally. Decision making to determine the best employee can be done by the company by assessing the performance that has been carried out by its employees at CV. Maju Jaya is influenced by several criteria, namely Knowledge, Discipline, Responsibility, Attendance, Teamwork. This decision support system uses the Simple Additive Weighting (SAW) method where the decision-making process can be calculated based on the calculation of the weight of each criterion, so that the best employees in the company can be selected quickly.
IMPLEMENTASI ALGORITMA TF-IDF DAN SUPPORT VECTOR MACHINE TERHADAP ANALISIS PENDETEKSI KOMENTAR CYBERBULLYING DI MEDIA SOSIAL TIKTOK Romindo Romindo; Jefri Junifer Pangaribuan; Okky Putra Barus
Device Vol 13 No 1 (2023): Mei
Publisher : Fakultas Teknik dan Ilmu Komputer (FASTIKOM) UNSIQ

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32699/device.v13i1.5260

Abstract

Cyberbullying is the act of sending text, images, or videos using the internet, mobile phones, or other devices with the aim of hurting and shaming other people. Cyberbullying is often done through several social media platforms, one of which is through comments on the TikTok application. According to a report by We Are Social, TikTok has 1.4 billion monthly active users aged 18 and above globally. Indonesia currently ranks second in the world in terms of active TikTok users. As a result, the potential for cyberbullying instances will grow as the number of users grows. By using data mining, the public can create a detection system, which can perform analysis on comments in the TikTok application. The method used is Term Frequency-Inverse Document Frequency (TF-IDF) and Support Vector Machine (SVM). The stages passed are to collect comments that are labelled manually. Then, text preprocessing, tokenizing, and weighting were carried out with TF-IDF. Then, implement the Support Vector Machine algorithm to detect cyberbullying comments. This study uses 80% training data and 20% testing data. From the performance results of the Support Vector Machine algorithm, 88% overall accuracy, 88% precision, 96% recall, and 92% f1-score were obtained in detecting cyberbullying comments on social media TikTok.
Sosialisasi Sistem Point of Sale Berbasis Web pada Toko Rita Kosmetik Elvina Winarto; Romindo Romindo; Yudhistira Adhitya Pratama; Okky Putra Barus
ABDIKAN: Jurnal Pengabdian Masyarakat Bidang Sains dan Teknologi Vol. 2 No. 2 (2023): Mei 2023
Publisher : Yayasan Literasi Sains Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55123/abdikan.v2i2.2282

Abstract

Nowadays, cosmetics have become a daily necessity for women, men, and children. The cosmetics in question are not just make-up cosmetics such as powder or blush. Products such as shampoo and soap are also included as cosmetics that fall into the skincare category. Various phenomena or trends that enter Indonesia affect cosmetic sales. Rita Kosmetik Store has been running since 2004 and until now Rita Kosmetik Store is still running its business manually, using paper media. The manual process has many weaknesses, including human error, long data management, weak supervision, and low data integrity. The author then designed a web-based point-of-sale system using Laravel with a MySQL database based on these problems. The resulting point-of-sale system helps Rita Kosmetik Store owners and employees in recording, collecting, and managing business transactions, inventory is properly tracked, and the resulting reports do not take much time and are more accurate.
OPTIMALISASI ALGORITMA C4.5 TERHADAP METODE DECISION TREE DALAM MENENTUKAN PLAFON KREDIT NASABAH Romindo Romindo; Okky Putra Barus; Jefri Junifer Pangaribuan
Device Vol 14 No 1 (2024): Mei
Publisher : Fakultas Teknik dan Ilmu Komputer (FASTIKOM) UNSIQ

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32699/device.v14i1.6877

Abstract

The most basic banking activity is collecting money and buying money from the whole society. Then sell the collected money by directing it to the community through credit or credit. However, it is often found that customers are unable to pay their receivables based on the amount of receivables which often exceeds the specified payment period. Therefore, banking companies must know the ability to pay customers by providing credit limits to avoid losses. The purpose of this study was to analyze the data using the Decision Tree method with the C4.5 Algorithm on the report data of BPR Pijer Podi Kekelengen receivables in order to determine the customer's credit ceiling. From the data obtained from the accounts receivable report, the company produces 5 attributes, namely Payments, Receivables, Transactions, Recommendations, and Ceiling where the decision label is Ceiling. After testing the report data at BPR Pijer Podi Kekelengen using the Decision Tree method with the C4.5 Algorithm, it is concluded that if the ceiling is large, the payment is not good.
UNLEASHING THE POWER OF SVM AND KNN: ENHANCED EARLY DETECTION OF HEART DISEASE Jefri Junifer Pangaribuan; Ade Maulana; Romindo Romindo
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 10 No. 2 (2024): JITK Issue November 2024
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v10i2.5719

Abstract

Heart disease is a fatal illness responsible for approximately 36% of deaths in 2020. Therefore, it is important to pay attention to and better anticipate the risk of heart disease. One technological contribution that can be made is through information related to the risk of heart disease. Classification techniques in data mining can be used to diagnose and identify the risk of heart disease earlier by processing medical data and making predictions. This study compares the effectiveness of two classification algorithms, Support Vector Machine (SVM) and K-Nearest Neighbor (KNN), in predicting the risk of heart disease using a Kaggle dataset consisting of 303 records with 14 attribute columns. The data is divided into 70% for training and 30% for testing. The software used in this study is Orange Data Mining to build the SVM and KNN models. The results show that the SVM accuracy is 85.6%, while KNN achieves 81.1%. Based on the confusion matrix, the SVM algorithm has a lower error rate compared to KNN. In conclusion, the SVM algorithm is superior to KNN in predicting the risk of heart disease. These findings indicate that SVM has a better potential in identifying individuals at high risk of experiencing a heart attack. This research can contribute to the development of a more accurate medical decision support system for early detection of heart disease.
Analisis Sentimen Komentar Toxic pada Video Musik YouTube Menggunakan Metode Naive Bayes Romindo Romindo; Kevin Bastian Sirait; Valentino Riffan; Chailine Garcia Wijaya
Jurnal Minfo Polgan Vol. 15 No. 1 (2026): Artikel Penelitian
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/jmp.v15i1.16190

Abstract

Platform berbagi video YouTube telah menjadi ruang interaksi digital yang masif, namun seiring pertumbuhannya, fenomena komentar toxic atau negatif semakin marak ditemukan pada konten-konten populer yang bersifat kontroversial. Penelitian ini bertujuan untuk menganalisis pola sentimen komentar toxic pada video musik "Hozier - Take Me to Church" serta mengukur performa algoritma Naive Bayes dalam melakukan klasifikasi sentimen secara otomatis. Data dikumpulkan menggunakan YouTube API dengan total 100.000 data komentar berbahasa Inggris, yang setelah melalui proses pembersihan menghasilkan 51.348 data valid. Proses text preprocessing mencakup lowercasing, penghapusan URL, tanda baca, angka, dan emotikon, diikuti tahap contraction, tokenization, penghapusan stopwords, serta lemmatization menggunakan library Natural Language Toolkit (NLTK). Data kemudian dilabeli ke dalam tiga kelas sentimen: positif, negatif, dan netral, sebelum diklasifikasi menggunakan algoritma Naive Bayes. Evaluasi performa model dilakukan dengan confusion matrix yang menghasilkan nilai akurasi sebesar 74%. Presisi untuk kelas negatif mencapai 96%, netral 97%, dan positif 64%. Nilai recall kelas negatif sebesar 57%, netral 49%, dan positif 99%. Sedangkan F1-score kelas negatif sebesar 71%, netral 65%, dan positif 78%. Dari total data yang diproses, ditemukan 8.475 komentar toxic (negatif), menunjukkan bahwa sebagian besar audiens merespons video tersebut secara positif. Hasil ini membuktikan bahwa algoritma Naive Bayes memiliki kemampuan yang memadai dalam klasifikasi sentimen komentar media sosial.
Klasifikasi Tingkat Kelunturan Warna Kain Menggunakan KNN, SVM, dan Random Forest Romindo Romindo; Triandes Sinaga; Kevin Bastian Sirait; Arosochi Yosua Daeli; Jepronel Saragih
INSOLOGI: Jurnal Sains dan Teknologi Vol. 5 No. 3 (2026): Juni 2026
Publisher : Yayasan Literasi Sains Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55123/insologi.v5i3.8721

Abstract

The laundry industry faces challenges in maintaining service quality, particularly regarding fabric color fading after washing. Assessments that are still performed manually tend to be subjective and inconsistent, so a more objective automated classification system is required. This study aims to apply and compare three algorithms, namely KNN, SVM, and Random Forest, to classify the level of fabric color fading based on digital images. The features used comprise color in the RGB and HSV spaces as well as shape in the form of area and shape ratio, all extracted automatically. A total of 300 images were divided into 250 training data and 50 testing data, then mapped into three categories, namely not faded, fairly faded, and faded. The testing results show that Random Forest delivers the best performance with an accuracy of 0.96, followed by SVM at 0.94 and KNN at 0.88. All models faced difficulties in recognizing the minority class due to data imbalance. This study proves that the machine learning approach, particularly Random Forest, is able to assess color fading levels more accurately and consistently than manual evaluation, while supporting quality control in the laundry industry.
Analisis Kualitas Wine Menggunakan Machine Learning dengan Pendekatan SMOTE dan Seleksi Fitur Triandes Sinaga; Kevin Bastian Sirait; Jefri Junifer Pangaribuan; Okky Putra Barus; Romindo Romindo
INSOLOGI: Jurnal Sains dan Teknologi Vol. 4 No. 3 (2025): Juni 2025
Publisher : Yayasan Literasi Sains Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55123/insologi.v4i3.5436

Abstract

Conventional wine quality assessment remains reliant on subjective expert judgment, which introduces potential bias and inconsistency in quality control processes. This study aims to develop an objective and automated machine learning-based classification model to enhance the accuracy of wine quality prediction. To address the issue of class imbalance, the Synthetic Minority Over-sampling Technique (SMOTE) was applied, along with ANOVA F-test-based feature selection to optimize model performance. The White Wine Quality dataset from the UCI Machine Learning Repository (4,898 samples, 11 numerical features) was utilized to evaluate five classification algorithms: Naïve Bayes, Decision Tree, Random Forest, Support Vector Machine (SVM), and K-Nearest Neighbors (KNN). Before SMOTE application, the Random Forest model achieved an accuracy of only 67.55%. After implementing SMOTE and parameter tuning, the Random Forest (Tuned) model demonstrated the best performance with 90.29% accuracy, 89.99% precision, 90.29% recall, and 89,97%.  % F1-score. Additionally, Decision Tree and KNN algorithms also exhibited notable improvements. SMOTE effectively balanced extreme minority class representations (quality levels 3 and 9). The most influential features in quality classification were alcohol content, density, and chlorides. These findings indicate that the proposed framework offers a reliable, objective, and scalable solution for automated wine quality control in industrial production environments.
Kuantifikasi Prioritas Pengembangan Fitur Aplikasi dengan Kerangka Outcome-Driven Innovation: Studi Kasus Aplikasi Pembelajaran Bahasa Asing Kevin Bastian Sirait; Nicholas Dickson; Romindo Romindo
INSOLOGI: Jurnal Sains dan Teknologi Vol. 4 No. 6 (2025): Desember 2025
Publisher : Yayasan Literasi Sains Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55123/insologi.v4i6.6762

Abstract

This study aims to identify user satisfaction, importance, and development priorities for the English Language Speech Assistant (ELSA) application by integrating Outcome-Driven Innovation (ODI) and sentiment analysis methods. Using reviews collected from the Google Play Store via ScrapeStorm, two sentiment models were applied. Namely, Valance Aware Dictionary and sEntiment Reasoner (VADER) for overall sentiment polarity and Aspect-Based Sentiment Analysis (ABSA) for feature-spesific sentiment extraction. The result show that 86.23% of user reviews express positive sentiments, reflecting high satisfaction with ELSA’s functionality and learning experience. Correlation testing confirms strong validity between VADER and ABSA models (r = 0.98), and moderate alignment with user rating (r  0.55), confirming both construct and external validity. The ODI analysis further identifies Pronunciation as the highest-priority aspect (opportunity = 12.1), followed by Learning (opportunity = 2.6), highlighting key areas for innovation. Other aspects, such as Support and Subscription, show balanced performance with low opportunity values. The integration of ODI and sentiment analysis provide a data-driven framework for product improvement, enabling developers to prioritize enhancements based on user-perceived value. The findings contribute to informed strategic decision-making in the development of digital language-learning applications.
Co-Authors Ade Maulana Angelia Putriana Arosochi Yosua Daeli Barus, Okky Putra Chailine Garcia Wijaya Elvina Winarto Jepronel Saragih Jusin Jusin Kevin Bastian Sirait Muhammad Thafa Kurniawan Nicholas Dickson Pangaribuan, Jefri Junifer Rudolfo Rizki Damanik Siringoringo, Rimmar Triandes Sinaga Valentino Riffan Yudhistira Adhitya Pratama