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All Journal TELKOMNIKA (Telecommunication Computing Electronics and Control) 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 Journal of Data Mining and Information Systems
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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Design of an Android-Based Sitting Posture Detection Application Using Deep Learning Jhonshen Lim; Octara Pribadi; Andy
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 5 No. 3 (2026): June 2026
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v5i3.2341

Abstract

Prolonged poor sitting posture is a major cause of musculoskeletal disorders including lower back pain and spinal abnormalities. This study designs and implements PosturApp, a deep learning-based Android application for real-time sitting posture detection using Kotlin. A Multi-Layer Perceptron (MLP) model was trained on 3,526 keypoint datasets sourced from the Kaggle public dataset (Posture Recognition) and direct image capture using an Android front camera, extracting 66 coordinate values from 33 body landmarks via MediaPipe BlazePose. The model was converted to TensorFlow Lite (TFLite) format at approximately 78 KB for on-device inference without internet connectivity. Evaluation results show an accuracy of 97.81% with precision 0.99, recall 0.99, and F1-Score 0.98. The application provides real-time visual feedback through interface color changes and corrective notifications, along with a gallery-based classification feature. Functional testing across eight posture scenarios yielded entirely correct results with confidence values ranging from 59% to 99%.
Stock Price Prediction In Indonesia Using Deep Learning With Long Short-Term Memory Algorithm Kevin; Octara Pribadi; Leony Hoki
Jurnal Armada Informatika Vol 10 No 1 (2026): Juni
Publisher : STMIK Methodist Binjai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36520/jai.v10i1.221

Abstract

Stock price prediction is one of the complex problems in the financial world because it is non-linear and influenced by many external factors. This study aims to apply the Long Short-Term Memory (LSTM) algorithm, a variant of Recurrent Neural Network (RNN) within the Deep Learning framework, to predict the closing price of banking sector stocks in the Indonesian capital market. The data used comes from five major banking issuers on the Indonesia Stock Exchange, namely BBCA, BBTN, BBRI, BBMRI, and BBNI, obtained from the Kaggle platform. The method used is a literature study by reviewing relevant literature related to the application of LSTM to financial time series data. The main parameter configurations include 50 hidden units, 100 epochs, a batch size of 32, a learning rate of 0.001, and a ReLU activation function with Adam optimizer. Model evaluation was carried out using the Root Mean Square Error (RMSE) metric of 6.61%, Mean Absolute Error (MAE) of 1.74%, Mean Square Error (MSE) of 43.69%, and a coefficient of determination R² of 0.718. The results show that the LSTM model is able to produce predictions close to actual values, reduce the risk of overfitting, and can be used as an investment decision-making tool for investors in the Indonesian stock market.
A comparative MRI-based study of ResNet-152 and novel deep learning approaches for early Alzheimer’s disease classification Kelvin Leonardi Kohsasih; Octara Pribadi; Andy Andy; Daniel Smith Sunario
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 24, No 2: April 2026
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v24i2.27576

Abstract

Alzheimer’s disease (AD) is the leading cause of dementia, making early-stage detection essential for timely intervention. Most prior studies have focused on binary AD classification, which limits sensitivity to disease progression. This study addressed this gap by evaluating whether tailored convolutional neural network (CNN) architectures could improve stage-aware classification using a publicly available magnetic resonance imaging (MRI) dataset containing 35,984 images across four diagnostic categories. The dataset underwent grayscale conversion, resizing, contrast enhancement, normalization, and class balancing prior to model development. Four models were trained and compared: ResNet-152, a custom multiclass CNN, a one-vs-one (OvO) model, and a one-vs-rest (OvR) model. Performance was measured using accuracy, precision, recall, F1 score, and confusion-matrix–based metrics. The custom multiclass CNN achieved the strongest performance, yielding the highest accuracy and balanced results across all evaluation metrics. These findings demonstrate the value of systematically comparing decomposition strategies for multi-stage Alzheimer’s detection and highlight the potential of the proposed approach to enhance early diagnostic support. Future work may incorporate multimodal inputs or hybrid architectures to improve sensitivity to subtle structural changes and further strengthen clinical applicability.
Sistem Pemantauan Kualitas Udara Berbasis IoT di Peternakan Yakin Telur Hendri; Octara Pribadi; Jackri Hendrik
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 Johanes Terang Kita Perangin Angin; Ari Rizkita; Robet Robet; Octara Pribadi
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.
DETEKSI BERITA HOAKS BAHASA INDONESIA MENGGUNAKAN KOMBINASI TF-IDF DAN K-NEAREST NEIGHBOR Ari Rizkita; Johanes Terang Kita Perangin Angin; Robet Robet; Octara Pribadi; Iqbal Giffari Ritonga
Jurnal TIMES Vol 15 No 1 (2026): Jurnal TIMES
Publisher : STMIK TIME

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51351/jtm.15.1.2026933

Abstract

Penyebaran berita hoaks di media digital Indonesia telah menjadi tantangan serius yang mengancam stabilitas sosial dan ketertiban publik. Berdasarkan data Kementerian Komunikasi dan Informatika, jumlah isu hoaks terus meningkat secara signifikan setiap tahunnya, sehingga diperlukan sistem deteksi otomatis yang cepat dan akurat. Penelitian ini bertujuan untuk mengimplementasikan dan menganalisis performa kombinasi metode ekstraksi fitur Term Frequency-Inverse Document Frequency (TF-IDF) dan algoritma K-Nearest Neighbor (KNN) dalam mengklasifikasikan berita hoaks berbahasa Indonesia. Dataset yang digunakan berjumlah 1.000 entri seimbang yang bersumber dari TurnBackHoax.id sebagai representasi hoaks, serta Antaranews, Kompas, dan Detik sebagai representasi berita valid. Eksperimen dilakukan dengan pembagian data latih dan uji sebesar 80:20 serta pengujian iteratif pada nilai parameter K ganjil (3, 5, 7, 9, dan 11). Hasil penelitian menunjukkan bahwa model mencapai performa maksimal dengan nilai akurasi, presisi, dan recall sebesar 100% pada seluruh skenario nilai K. Hal ini mengindikasikan bahwa pembobotan statistik TF-IDF mampu membedakan pola kosakata antara klarifikasi hoaks dan teks jurnalistik secara sempurna. Kesimpulannya, algoritma KNN terbukti sangat efektif dan efisien secara komputasi untuk digunakan sebagai sistem penyaring misinformasi pada media digital di Indonesia.
Perancangan Aplikasi SIAKAD Kampus STMIK Time Berbasis Android Felix Felix; Octara Pribadi; Robby Wijaya
JDMIS: Journal of Data Mining and Information Systems Vol. 4 No. 2 (2026): August 2026
Publisher : Yayasan Pendidikan Penelitian Pengabdian Algero

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54259/jdmis.v4i2.7785

Abstract

The Academic Information System (SIAKAD) is the main pillar in administrative services in higher education. Currently, STMIK TIME already has a web-based SIAKAD portal, but accessibility via mobile devices is still limited. This research aims to design and develop the frontend of the STMIK TIME Campus SIAKAD application based on Android to facilitate students in accessing academic data in real-time. The application development method uses the Flutter framework with the Dart programming language, implementing Clean Architecture patterns and BLoC State Management to maintain scalability and ease of code maintenance. Data integration is carried out through REST API to ensure synchronization between the mobile application and the web portal server. The result of this research is a mobile application prototype that includes features for KRS registration, KHS checking, academic transcripts, and student profile management. Internal testing shows that the implementation of a modular code structure and efficient state management is able to provide stable and responsive application performance for users.
Co-Authors Ali, Rabei Raad Andi Andi Andy Andy Andy Ari Rizkita Ari Rizkita Arnold Adimabua Ojugo Bayu Teta Bimo Haryo Setyoko Carles Juliandy Daniel Happy Putra Daniel Smith Sunario david De Rosal Ignatius Moses Setiadi Destisonya Buulolo Edi Edi Wijaya Eferhire Valentine Ugbotu ENDRI ENDRI Etika Kartikadarma Felix Felix Feriani Astuti Tarigan Halim, Marwa Hendri Hendri Hendri Hendri Hoki, Leony Iqbal Giffari Ritonga Jackri Hendrik Jackri Hendrik Jhonshen Lim Johanes Terang Kita Perangin Angin Juan Charles Juliyanto Kelvin Leonardi Kohsasih Kevin Leony Hoki Md Kamruzzaman Sarker Nicholas Tio Panjaitan, Tabitha Martha Agustine Perangin Angin, Johanes Pieter Octaviandy, Pieter Rizkita, Ari Robby Wijaya 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