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All Journal CommIT (Communication & Information Technology) Jurnal TIMES Sinkron : Jurnal dan Penelitian Teknik Informatika Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control JOURNAL OF APPLIED INFORMATICS AND COMPUTING METHOMIKA: Jurnal Manajemen Informatika & Komputerisasi Akuntansi Jiko (Jurnal Informatika dan komputer) Jurnal Sistem Komputer dan Informatika (JSON) Jurnal Teknik Informatika (JUTIF) Bulletin of Computer Science Research Jurnal Info Sains : Informatika dan Sains Jurnal Armada Informatika Journal of Artificial Intelligence and Engineering Applications (JAIEA) Journal of Computing Theories and Applications
Octara Pribadi
Sekolah Tinggi Manajemen Informatika Dan Komputer (STMIK) TIME
Automotive Engineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Energy Engineering

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Perancangan Aplikasi Kids Memory Game Berbasis Android Octaviandy, Pieter; Pribadi, Octara; Juliyanto
Jurnal TIMES Vol 9 No 1 (2020)
Publisher : STMIK TIME

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (798.214 KB) | DOI: 10.51351/jtm.9.1.2020618

Abstract

Child growth is the most important moment for a child. Children who are still young must be equipped with useful education so that later it will be useful when the child is growing up. Children's memory is very important to be trained as a child because with the process of memory training it can train the child to be fast in learning something good whether it's a lesson or other general matters. In practice, the learning process to train the child's memory is still done manually, namely by picture books, this is less interesting for the child because children prefer to play while learning. Besides the existing textbooks are quite boring because there are no games or sound effects that attract the child's interest to learn. Therefore, the above problems will be designed an educational game that trains children's memory with the concept of play. The results of this study are in the form of an educational game called Kids Memory Game which is used as a media for children's education and media to train children's memory.
Sistem Kendali Jarak Jauh Air Conditioner (AC) Berbasis IoT Pribadi, Octara
Jurnal TIMES Vol 9 No 1 (2020)
Publisher : STMIK TIME

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (3.436 KB) | DOI: 10.51351/jtm.9.1.2020622

Abstract

Dalam penelitian ini penulis merancang sistem kendali jarak jauh untuk mengendalikan Air Conditioner (AC) menggantikan remote konvensional. Penulis menggunakan mikrokontroler ESP8266 dan Infrared Led (IR Led) serta aplikasi blynk. Dalam penelitian ini akan dilihat apakah sistem yang dirancang dapat berjalan dengan baik, dengan pengkondisian jaringan WiFi yang digunakan secara khusus untuk sistem yang dirancang. Diharapkan dalam penelitian ini bisa menjadi referensi bagi peneliti lain dalam mengembangkan peneltian serupa, dan tidak menutup kemungkinan juga dapat dihasilkan produk jadi siap pakai dikemudian harinya.
Design of a Web-Based School Profile at Northern Green School Medan Using the Importance Performance Analysis Method Destisonya Buulolo; Jackri Hendrik; Octara Pribadi
Jurnal Info Sains : Informatika dan Sains Vol. 15 No. 01 (2025): Informatika dan Sains , 2025
Publisher : SEAN Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

School profile website in today's education world is very important, to convey information and school identity to the public. Currently, SD Northern Green School Medan does not have one that can function as a means of promotion that presents various important information such as school name, location, vision and mission, student achievements, gallery, and contacts that can be reached by parents and the public who want to register their children at SD Northern Green School Medan. This study aims to design a web-based school profile that is able to meet the information needs of users. The method applied in this study is the Importance Performance Analysis (IPA) Method, which helps in identifying the required attributes and evaluating their performance from the user's perspective. Data were obtained through direct observation and distributing questionnaires to 50 respondents. The results of the analysis using the Importance Performance Analysis (IPA) method show that elements such as school profile, achievements, activity gallery, and contact information are important parts that must be on the SD Northern Green School Medan website. This website was developed using HTML, PHP, and MySQL. With the presence of this website, it is hoped that the school will be able to be more effective in conveying information and improving the reputation of SD Northern Green School Medan in the current digital era.
Sistem Pemantauan Kualitas Udara Berbasis IoT di Peternakan Yakin Telur Hendri; Pribadi, Octara; Hendrik, Jackri
METHOMIKA: Jurnal Manajemen Informatika & Komputerisasi Akuntansi Vol. 9 No. 1 (2025): METHOMIKA: Jurnal Manajemen Informatika & Komputersisasi Akuntansi
Publisher : Universitas Methodist Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46880/jmika.Vol9No1.pp145-150

Abstract

Air quality in poultry farms plays a crucial role in animal productivity, as poor air quality can increase the risk of disease in chickens, cause stress, and ultimately reduce productivity and egg quality, leading to economic losses for farmers. Laying hens require stable and adequate temperature conditions during their growth period to ensure optimal development. The optimal temperature for laying hens during the brooding period (up to 14 days old) ranges between 30-32°C. A common issue faced by livestock farmers is the lack of adequate facilities to manage stress in livestock, which often hinders their ability to stabilize the air temperature in the chicken coop. Farmers often rely on manual methods to estimate and adjust the temperature inside the coop by feeling the heat, which is neither accurate nor efficient. This research aims to design an IoT-based air quality monitoring system at Yakin Telur Farm. The system is designed to monitor temperature, humidity, and ammonia gas levels in the chicken coop in real-time.
Akurasi K-Means dengan Menggunakan Cluster dan Titik Grid Terbaik pada Pemetaan Grid Interatif K-Means Perangin Angin, Johanes Terang Kita; Rizkita, Ari; Robet, Robet; Pribadi, Octara
METHOMIKA: Jurnal Manajemen Informatika & Komputerisasi Akuntansi Vol. 9 No. 1 (2025): METHOMIKA: Jurnal Manajemen Informatika & Komputersisasi Akuntansi
Publisher : Universitas Methodist Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46880/jmika.Vol9No1.pp127-129

Abstract

Traditional K-Means face 2 (two) main problems, namely: Determination of Initial Centroid and poor initial cluster. Determining the initial centroid using random numbers is one of the main problems in classical K-Means which results in low accuracy and long computation time. Likewise, determining the good centroid of each cluster without being accompanied by a process of paying attention to the performance of each cluster can also cause the accuracy value obtained is not good. This study will contribute to how the performance obtained by determining a good initial centroid is combined with the use of a good cluster. Determination of a good initial centroid is done by using the K-Means Grid Mapping which divides the determination of the centroid into several Grid Points. The result of this research is a combination of Iterative K-Means with Grid Mapping K-Means to become Iterative Grid Mapping K-Means which will get a good initial centroid and also a good cluster shown in the table of iris and abalone, comparison of the variables in the iris and abalone affecting the best cluster as a result.
Multi-Class Brain Tumor Segmentation and Classification in MRI Using a U-Net and Machine Learning Model Hendrik, Jackri; Pribadi, Octara; Hendri, Hendri; Hoki, Leony; Tarigan, Feriani Astuti; Wijaya, Edi; Ali, Rabei Raad
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 5 (2025): JUTIF Volume 6, Number 5, Oktober 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.5.5369

Abstract

Brain tumor diagnosis remains a critical challenge in medical imaging, as accurate classification and precise localization are essential for effective treatment planning. Traditional diagnostic approaches often rely on manual interpretation of MRI scans, which can be time-consuming, subjective, and prone to variability across radiologists. To address this limitation, this study proposes a two-stage framework that integrates machine learning (ML) based classifiers for tumor type recognition and a U-Net architecture for tumor segmentation. The classifier was trained to distinguish four tumor categories: glioma, meningioma, pituitary, and no tumor, while the U-Net model was employed to delineate tumor regions at the pixel level, enabling volumetric assessment. The novelty of this research lies in its dual focus that combines classification and segmentation within a single framework, which enhances clinical applicability by offering both diagnostic and spatial insights. Experimental results demonstrated that among the evaluated classifiers, XGBoost achieved the highest accuracy of 86 percent, surpassing other models such as Random Forest, SVC, and Logistic Regression, while the U-Net model delivered consistent segmentation performance across tumor types. These findings highlight the potential of hybrid ML and deep learning solutions to improve reliability, efficiency, and objectivity in brain tumor analysis. In real-world practice, the proposed framework can serve as a valuable decision-support tool, assisting radiologists in early detection, reducing diagnostic workload, and supporting personalized treatment strategies.
Klasifikasi Multikelas Tingkat Diabetes Berdasarkan Indikator Kesehatan Pasien Menggunakan Strategi One-vs-Rest Panjaitan, Tabitha Martha Agustine; Robet; Octara Pribadi
Jurnal Sistem Komputer dan Informatika (JSON) Vol. 7 No. 2 (2025): Desember 2025
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/json.v7i2.8985

Abstract

Diabetes is a non-communicable disease with a steadily increasing global prevalence. It often remains undiagnosed in its early stages, particularly during the prediabetic phase, which typically lacks noticeable symptoms. This study aims to develop a multi-class classification model to predict diabetes severity levels non-diabetic, prediabetic, and diabetic based on patient health indicators. A One-vs-Rest (OvR) strategy was employed, training each class against a combination of the others. The dataset was derived from the 2015 National Health Survey, comprising over 250,000 patient records with features such as blood pressure, body mass index, cholesterol levels, history of heart disease, and physical activity. Two machine learning algorithms, Logistic Regression and Random Forest, were applied to train the models. Class imbalance was addressed using the Synthetic Minority Over-sampling Technique (SMOTE). Evaluation metrics included accuracy, precision, recall, F1-score, and confusion matrix. The results show that the Random Forest model achieved an average accuracy of 93% and consistently high F1-scores, particularly for the prediabetic class of 98%. The most influential predictors were high blood pressure, obesity, and insufficient physical activity. This study contributes to the development of a reliable and efficient data-driven system for early diabetes risk detection.
Comprehensive Comparison of TF-IDF and Word2Vec in Product Sentiment Classification Using Machine Learning Models Sinaga, Asra Gretya; Robet, Robet; Pribadi, Octara
Journal of Applied Informatics and Computing Vol. 10 No. 1 (2026): February 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i1.11582

Abstract

Sentiment analysis supports data-driven decisions by turning product reviews into reliable polarity labels. We compare four text representations, TF-IDF, TF-IDF reduced via SVD, Word2Vec (trained from scratch), and a hybrid TF-IDF(SVD-300). Word2Vec, for sentiment classification of Indonesian Shopee product reviews from Kaggle (~2.5k texts). After normalization (with optional emoji handling and Indonesian stemming), ratings are mapped to binary sentiment (≤2 negative, ≥4 positive; 3 discarded). Each representation is evaluated with Logistic Regression, Support Vector Machines (linear/RBF), Naive Bayes, and Random Forest under stratified 5-fold cross-validation. TF-IDF with Logistic Regression (C=1.0) yields the best results (F1-macro = 0.816 ± 0.026; Accuracy = 0.816 ± 0.026), with LinearSVC as a strong runner-up. Word2Vec (scratch) performs lower, consistent with limited data being insufficient to learn stable embeddings, while the hybrid representation offers only modest gains over Word2Vec and does not surpass TF-IDF. These findings indicate that TF-IDF is the most reliable and consistent representation for small, short-text review datasets, and they underscore the impact of feature design on downstream classification performance.
Image Encryption using Half-Inverted Cascading Chaos Cipheration De Rosal Ignatius Moses Setiadi; Robet Robet; Octara Pribadi; Suyud Widiono; Md Kamruzzaman Sarker
Journal of Computing Theories and Applications Vol. 1 No. 2 (2023): JCTA 1(2) 2023
Publisher : Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33633/jcta.v1i2.9388

Abstract

This research introduces an image encryption scheme combining several permutations and substitution-based chaotic techniques, such as Arnold Chaotic Map, 2D-SLMM, 2D-LICM, and 1D-MLM. The proposed method is called Half-Inverted Cascading Chaos Cipheration (HIC3), designed to increase digital image security and confidentiality. The main problem solved is the image's degree of confusion and diffusion. Extensive testing included chi-square analysis, information entropy, NCPCR, UACI, adjacent pixel correlation, key sensitivity and space analysis, NIST randomness testing, robustness testing, and visual analysis. The results show that HIC3 effectively protects digital images from various attacks and maintains their integrity. Thus, this method successfully achieves its goal of increasing security in digital image encryption
Integrating Hybrid Statistical and Unsupervised LSTM-Guided Feature Extraction for Breast Cancer Detection De Rosal Ignatius Moses Setiadi; Arnold Adimabua Ojugo; Octara Pribadi; Etika Kartikadarma; Bimo Haryo Setyoko; Suyud Widiono; Robet Robet; Tabitha Chukwudi Aghaunor; Eferhire Valentine Ugbotu
Journal of Computing Theories and Applications Vol. 2 No. 4 (2025): JCTA 2(4) 2025
Publisher : Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/jcta.12698

Abstract

Breast cancer is the most prevalent cancer among women worldwide, requiring early and accurate diagnosis to reduce mortality. This study proposes a hybrid classification pipeline that integrates Hybrid Statistical Feature Selection (HSFS) with unsupervised LSTM-guided feature extraction for breast cancer detection using the Wisconsin Diagnostic Breast Cancer (WDBC) dataset. Initially, 20 features were selected using HSFS based on Mutual Information, Chi-square, and Pearson Correlation. To address class imbalance, the training set was balanced using the Synthetic Minority Over-sampling Technique (SMOTE). Subsequently, an LSTM encoder extracted non-linear latent features from the selected features. A fusion strategy was applied by concatenating the statistical and latent features, followed by re-selection of the top 30 features. The final classification was performed using a Support Vector Machine (SVM) with RBF kernel and evaluated using 5-fold cross-validation and a held-out test set. Experimental results showed that the proposed method achieved an average training accuracy of 98.13%, F1-score of 98.13%, and AUC-ROC of 99.55%. On the held-out test set, the model reached an accuracy of 99.30%, precision of 100%, and F1-score of 99.05%, with an AUC-ROC of 0.9973. The proposed pipeline demonstrates improved generalization and interpretability compared to existing methods such as LightGBM-PSO, DHH-GRU, and ensemble deep networks. These results highlight the effectiveness of combining statistical selection and LSTM-based latent feature encoding in a balanced classification framework.
Co-Authors Ali, Rabei Raad Andi Andi Andy Arnold Adimabua Ojugo Bayu Teta Bimo Haryo Setyoko Carles Juliandy Daniel Happy Putra david De Rosal Ignatius Moses Setiadi Destisonya Buulolo Edi Edi Wijaya Eferhire Valentine Ugbotu ENDRI ENDRI Etika Kartikadarma Feriani Astuti Tarigan Halim, Marwa Hendri Hendri Hendri Hendri Hoki, Leony Jackri Hendrik Jackri Hendrik Jhonshen Lim Johanes Terang Kita Perangin Angin Juan Charles Juliyanto Md Kamruzzaman Sarker Nicholas Tio Panjaitan, Tabitha Martha Agustine Perangin Angin, Johanes Pieter Octaviandy, Pieter Rizkita, Ari Robert Robet Robet rusdiana Sinaga, Asra Gretya Sukiman Sukiman Suwanty Suwanty Suwanty Suwanty, Suwanty Suyud Widiono Suyud Widiono Syamsiar, Syamsiar Tabitha Chukwudi Aghaunor Vic Devlin Wilson