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All Journal Jurnal Ilmiah Informatika Komputer Jurnal Ilmiah Teknologi dan Rekayasa Jurnal Ilmiah Desain & Konstruksi Majalah Ilmiah Ekonomi Komputer Jurnal Ilmiah Kursor JOIV : International Journal on Informatics Visualization Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Creative Information Technology Journal METHOMIKA: Jurnal Manajemen Informatika & Komputerisasi Akuntansi Sebatik MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer Jurnal Informasi dan Komputer CCIT (Creative Communication and Innovative Technology) Journal METIK JURNAL Seminar Nasional Teknologi Informasi Komunikasi dan Administrasi [SEMINASTIKA] Publik: Jurnal Manajemen Sumber Daya Manusia, Administrasi dan Pelayanan Publik Indonesian Journal of Electrical Engineering and Computer Science Journal of Applied Data Sciences Journal of International Conference Proceedings Jurtik STMIK Bandung Kesejahteraan Bersama : Jurnal Pengabdian dan Keberlanjutan Masyarakat Prosiding SeNTIK STI&K
Prihandoko .
Universitas Gunadarma
Agriculture, Biological Sciences & Forestry Humanities Automotive Engineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Mathematics Social Sciences Transportation Other

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ANALISIS SENTIMEN REVIEW PENGGUNA APLIKASI DEPOK SINGLE WINDOW DI GOOGLE PLAY MENGGUNAKAN ALGORITMA SUPPORT VECTOR MACHINE Arie Wijaya; Prihandoko Prihandoko
Jurnal Ilmiah Informatika Komputer Vol 28, No 1 (2023)
Publisher : Universitas Gunadarma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35760/ik.2023.v28i1.7902

Abstract

Technology is developing rapidly, including in the world of government. The district government makes a web or mobile-based application with the aim of helping people in getting the services that the community deserves. The Depok Regency Government created a mobile-based public service application called Depok Single Window. Due to the importance of user reviews for the continuity of the DSW application, it is required to analyze the sentiment of reviews of the Depok Single Window application on Google Play Store. Sentiment analysis is carried out using the Support Vector Machine. The data used in this study were 733 reviews obtained from the scrapping. The scrapping is carried out by utilizing python library, namely google play scrapper as access to retrieve data. The results attained from this research are an accuracy value of 89.23% for the sentiment analysis of the Depok Single Window application, which means that the Support Vector Machine is good to be used to classify the Depok Single Window application review data into positive, negative and neutral.
Diagnosis Model in Smear-Negative Pulmonary Tuberculosis Using Faster R-CNN Nur Azizah; Benny Mutiara; Prihandoko Prihandoko; Po Abas Sunarya
CCIT (Creative Communication and Innovative Technology) Journal Vol 17 No 1 (2024): CCIT JOURNAL
Publisher : Universitas Raharja

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/ccit.v17i1.3061

Abstract

Background: One of the most important human organs in the respiratory system is the lung. The main function of the lung is the respiration process, which is responsible for pumping air into the body. The health of the lung organs is very important, because if this organ is disturbed it will affect the health of the rest of the body. One of the diseases that attacks the lungs is Tuberculosis (TB). TB disease can be cured, but if it is delayed in getting treatment it can increase the risk of death. Method: This research developed a Smear Negative Pulmonary Tuberculosis diagnosis model using the Deep Learning method using the Faster R-CNN algorithm. The data used in this research are x-ray images of the lungs at the Jakarta Repository Center - Indonesian Tuberculosis Eradication Center (JRC-PPTI) clinic, totaling 220 datasets. At the preprocessing stage, the images used for training and testing were used with a size of 1280 x 1280 to see the effect on the accuracy of the prediction results of the Faster RCNN model. The test results are in the form of accuracy values that reflect the performance of the Faster RCNN model in classifying normal (without TB) and abnormal (with TB) test data. Results: The research implementation carried out the training process and testing process for 75% of training images, and 25% for testing images. Training images are labeled using the Img label. In the testing stage of the faster RCNN model, the accuracy value was 62.04%, precision was 40.00%, recall was 64.52% and F1-score was 49.38%. Conclusion: From the results of this research it is concluded that the Faster RCNN model test results using the ResNet 50 model have an accuracy value of 62.04%, Precision of 40.00%, Recall of 64.52% and F1-score of 49.38%.
Peningkatan Kesadaran tentang Keamanan Informasi pada Masyarakat Aktif di Kabupaten Indramayu Anita Muliawati; Widya Cholil; Kraugusteeliana Kraugusteeliana; Ati Zaidiah; Tjahjanto Tjahjanto; Prihandoko Prihandoko; Azim Zaliha binti Abd Aziz
Kesejahteraan Bersama : Jurnal Pengabdian dan Keberlanjutan Masyarakat Vol. 2 No. 1 (2025): Januari : Kesejahteraan Bersama : Jurnal Pengabdian dan Keberlanjutan Masyaraka
Publisher : Lembaga Pengembangan Kinerja Dosen

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62383/bersama.v2i1.1018

Abstract

Lebu Digita (LeDig) is a program to realize a Smart Village or 'Smart Village' which aims to change the use of digital technology both in community services and in village government administration. The implementation of this program has used sufficient resources, both in terms of funds and other resources provided by the Indramayu Regency Government. This program aims to achieve the development strategy that has been planned by the Indramayu Regency Government and surrounding villages with the main goal of providing digital-based services for the Indramayu Regency Community. The use of a digital-based system must also be supported by strengthening user competencies through a digital literacy training program for village communities. This community service program aims to increase public awareness of information security in using digital media in the LeDig Program can increase productivity, expand markets, and create new jobs in Indramayu Regency. This community service activity also resulted in a measurement of the level of public awareness of information security using an instrument developed based on the COBIT 2019 Framework. From the results of this measurement, it can be determined the follow-up to the community service program that will be carried out next for the community using the LeDig program in Indramayu Regency, the implementation of which was carried out by a research team from the Faculty of Computer Science, UPN Veteran Jakarta in collaboration with partners from UNIZA, Malaysia.Lebu, Digital, Indramayu, Security, information.
Teknik Deep Learning Untuk Analisis Profil Daerah Sehat di Indonesia Maria Sri Wulandari; A Benny Mutiara; Asep Juarna; Prihandoko
Prosiding Seminar SeNTIK Vol. 2 No. 1 (2018): Prosiding SeNTIK 2018
Publisher : Lembaga Penelitian dan Pengabdian Kepada Masyarakat

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Abstract

Teknik Deep Learning Untuk Analisis Profil Daerah Sehat di Indonesia
Metode Penentuan Keyframe Berdasarkan Kesamaan Event Pada Pengelompokanframe Video Menggunakan Histogram Bin Warna HCL Ire Puspa Wardhani; Lussiana, ETP; Sunny Arief Sudiro; Sarifuddin Madenda; Prihandoko
Prosiding Seminar SeNTIK Vol. 2 No. 1 (2018): Prosiding SeNTIK 2018
Publisher : Lembaga Penelitian dan Pengabdian Kepada Masyarakat

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Abstract

Penelitian ini merupakan bagian dari penelitian tentang pencarian video berbasis konten dimana salah satu prosesnya adalah pengelompokan frame video dengan menggunakan metode penentuan keyframe berdasarkan kesamaan event dengan menggunakan histogram bin warna, salah satu tahapan proses penentuan keyframe diawali dengan proses ekstraksi file video yaitu memisahkan frame-frame dalam video tersebut, selanjutnya, frame-frame tersebut diekstraksi berdasarkan fitur warna local dan global dengan menggunakan histogram bin warna 3D. Prosespengelompokan frame-frame ini berada dalam satu event yang sama, sehinggahasilnya berupa cuplikan-cuplikan atau klip-klip video event. Metode penentuan keyframe ini sebagai ID yang akan merepresentasikan setiap klip video event dan menghasilkan tiga jenis data, pertama adalah keyframe-keyframe dengan fitur bin warnanya masing-masing sebagai ID dari setiap klip video event. Kedua adalah klip-klip video event yang masing-masing berisikan kelompok frame sesuai dengan event atau event saat pembuatan video, dan Ketiga adalah data file video itu sendiri. Tiga data ini kemudian disimpan dalam sebuah basis data,, dan metode penentuan keyframe ini sangat berhubungan dengan klip video event yang diwakilkannya dan setiap klip video event memiliki keterhubungan dengan file videonya sendiri sehingga nantinya akan berpengaruh terhadap hasil pencarian dan temu kembali video berbasis konten
Comparison of Distance Measurements Based on k-Numbers and Its Influence to Clustering Deny Jollyta; Prihandoko Prihandoko; Dadang Priyanto; Alyauma Hajjah; Yulvia Nora Marlim
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol. 23 No. 1 (2023)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v23i1.3078

Abstract

Heuristic data requires appropriate clustering methods to avoid casting doubt on the information generated by the grouping process. Determining an optimal cluster choice from the results of grouping is still challenging. This study aimed to analyze the four numerical measurement formulas in light of the data patterns from categorical that are now accessible to give users of heuristic data recommendations for how to derive knowledge or information from the best clusters. The method used was clustering with four measurements: Euclidean, Canberra, Manhattan, and Dynamic Time Warping and Elbow approach for optimizing. The Elbow with Sum Square Error (SSE) is employed to calculate the optimal cluster. The number of test clusters ranges from k = 2 to k = 10. Student data from social media was used in testing to help students achieve higher GPAs. 300 completed questionnaires that were circulated and used to collect the data. The result of this study showed that the Manhattan Distance is the best numerical measurement with the largest SSE of 45.359 and optimal clustering at k = 5. The optimal cluster Manhattan generated was made up of students with GPAs above 3.00 and websites/ vlogs used as learning tools by the mathematics and computer department. Each cluster’s ability to create information can be impacted by the proximity of qualities caused by variations in the number of clusters.
Cluster Validity for Optimizing Classification Model: Davies Bouldin Index – Random Forest Algorithm Prihandoko Prihandoko; Deny Jollyta; Gusrianty Gusrianty; Muhammad Siddik; Johan Johan
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol. 24 No. 1 (2024)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v24i1.4043

Abstract

Several factors impact pregnant women’s health and mortality rates. The symptoms of disease in pregnant women are often similar. This makes it difficult to evaluate which factors contribute to a low, medium, or high risk of mortality among pregnant women. The purpose of this research is to generate classification rules for maternal health risk using optimal clusters. The optimal cluster is obtained from the process carried out by the validity cluster. The methods used are K-Means clustering, Davies Bouldin Index (DBI), and the Random Forest algorithm. These methods build optimum clusters from a set of k-tests to produce the best classification. Optimal clusters comprising cluster members withstrong similarities are high-dimensional data. Therefore, the Principal Component Analysis (PCA) technique is required to evaluate attribute value. The result of the research is that the best classification rule was obtained from k-tests = 22 on the 20th cluster, which has an accuracy of 97% to low, mid, and high risk. The novelty lies in using DBI for data that the Random Forest will classify. According to the research findings, the classification rules created through optimal clusters are 9.7% better than without the clustering process. This demonstrates that optimizing the data group has implications for enhancing the classification algorithm’s performance.
Improving University Ranking Robustness Using Rank Geometric Weight Integration with CoCoSo Method for Reducing Ordinal Weighting Instability Septi Andryana; Teddy Mantoro; Achmad Benny Mutiara; Ernastuti Ernastuti; Prihandoko Prihandoko; Aris Gunaryati
Journal of Applied Data Sciences Vol 7, No 1: January 2026
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v7i1.1024

Abstract

This study lies in the field of decision support systems, focusing on the application of Multi-Criteria Decision Making (MCDM) for ranking alternatives based on predefined organizational criteria. A persistent challenge in this domain is the instability and subjectivity of ordinal weighting methods - such as Rank Order Centroid (ROC), Rank Sum (RS), Rank Reciprocal (RR), and Rank Order Distribution (ROD), which derive weights solely from rank positions, often leading to inconsistent and unreliable outcomes. To address this, this study introduces Rank Geometric (RG) weights, a geometric mean aggregation of ROC, RS, RR, and ROD designed to reduce subjectivity, stabilize weight distribution, and enhance robustness. By using the Combined Compromise Solution (CoCoSo) method, the RG against Times Higher Education’s (THE) official weights were evaluated, and the four individual ordinal methods, applied to the top 10 Indonesian universities across five THE 2025 ranking criteria. Empirical results show that RG-CoCoSo produces stronger and more consistent correlations with THE’s rankings than THE-CoCoSo, as validated by Spearman and Pearson correlation tests, with a p-value of 0.0251. This study contributes a practical, data-driven weighting framework that strengthens the reliability of MCDM-based institutional performance evaluation and can be generalized to other ranking contexts.
Integrasi Algoritma YOLOv8 dan Streamlit untuk Visualisasi Real-Time dan Akurat dalam Penghitungan Kerumunan di Kawasan Stasiun Bekasi Prihandoko Prihandoko; Sri Agustina Rumapea; Abdul Hanif Pratama
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.pp179-187

Abstract

Crowd management in public transportation areas has become a critical challenge with the rise of urban populations. This study develops a real-time web-based people detection and counting system by integrating the YOLOv8 algorithm with the Streamlit framework. A case study was conducted at the entrance of Bekasi Station. The model was developed using the AI Project Life Cycle approach, and the system was built following the Waterfall methodology. Data were obtained from video recordings, which were extracted into images, annotated, and processed into training and testing datasets. The YOLOv8 model was trained for 50 epochs, yielding strong performance with an mAP@0.5 of 91.7%, a maximum precision of 93.6%, and an F1-score of 87%. Tests on 15 images showed an average accuracy of 80.37% and an error rate of 19.63%. The model's performance declined on out-of-dataset images due to variations in lighting and extreme crowd density. The system was tested using black-box testing and demonstrated that all main features—image upload, object detection, visualization, and result download—functioned correctly. The system has been successfully deployed on Streamlit Cloud. These results indicate that the system offers a practical, lightweight, and responsive solution to support crowd monitoring in public areas. In future development phases, the system can be extended to support real-time video stream processing and integrated with an object tracking and classification module to accurately identify and differentiate the ingress and egress flow of individuals within a defined surveillance area.
Co-Authors A Benny Mutiara Abdul Hanif Pratama Achmad Benny Mutiara Ahmad Fajri Alyauma Hajjah Anita Muliawati Arie Wijaya Aris Gunaryati Armaini Akhirson Asep Juarna Azim Zaliha binti Abd Aziz Benny Mutiara Bertalya Bertalya Citra Handayani Dadang Priyanto Deny Jollyta Deny Jollyta Dyah Mieta Setyawati Ernastuti Ernastuti Febrian Iftikhar Irawan Gusrianty Gusrianty Heruzulkifli Rowa Heruzulkifli Rowa Ire Puspa Wardhani Johan Johan Kraugusteeliana Kraugusteeliana Lilis Setyowati Lumadi Lumadi Lussiana, ETP Maria Sri Wulandari Marlim, Yulvia Nora Muhaji Bayu Suryawan Muhammad Hadi Muhammad Siddik Mutiara AB Nanny Raras Setyoningrum Nova Agustina Nur Azizah Nurahman Nurmayanti Nurmayanti Po Abas Sunarya Prasetyo E Putri Lannidya Parameswari Rehulina Apriyanti Rr. Dharma Tintri Edi Raras Saeful Hamdi Sarifuddin Madenda Septi Andryana Sigit Widiyanto Sri Agustina Rumapea Sunny Arief Sudiro Syahifa Rahmita Irlianti Syaikhuddin, Muhammad Miftakhul Taghfirul Azhima Yoga Siswa Teddy Mantoro Tjahjanto Tjahjanto Widya C Widya Cholil Zaidiah, Ati