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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 Jurnal Teknologi Informasi dan Ilmu Komputer JOIV : International Journal on Informatics Visualization Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Creative Information Technology Journal Sebatik MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer Jurnal Informasi dan Komputer Energy : Jurnal Ilmiah Ilmu-Ilmu Teknik 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 World Science Journal of International Conference Proceedings Jurtik STMIK Bandung Kesejahteraan Bersama : Jurnal Pengabdian dan Keberlanjutan Masyarakat Prosiding SeNTIK STI&K
Prihandoko .
Universitas Gunadarma
Aerospace Engineering Agriculture, Biological Sciences & Forestry Humanities Automotive Engineering Chemical Engineering, Chemistry & Bioengineering 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 Energy Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Social Sciences Transportation Other

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EVALUATION OF PERSONNEL INFORMATION SYSTEM HUMAN RESOURCES DEVELOPMENT AGENCY HUMAN RESOURCES TRANSPORTATION Citra Handayani; Prihandoko Prihandoko
Publik: Jurnal Manajemen Sumber Daya Manusia, Administrasi dan Pelayanan Publik Vol 10 No 1 (2023): Publik: Jurnal Manajemen Sumber Daya Manusia, Administrasi dan Pelayanan Publik
Publisher : Universitas Bina Taruna Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37606/publik.v10i1.579

Abstract

This study aims to determine the extent to which the personnel information system at the BPSDM of the Ministry of Transportation is running. This is done with the HOT FIT Model approach which tests several things on these variables including technology, Human, and Organization. As a result, the factory has a very large contribution of 63.6%. Based on these results, it can be concluded that the human factory element has a significant role, the thing that can be done is to provide regular training and continue to improve the quality of the Human factory.
ANALISIS TINGKAT KEPUASAN PENGGUNA SISTEM INFORMASI UJIAN AKHIR SEMESTER MENGGUNAKAN METODE END USER COMPUTING SATISFACTION (EUCS) Nanny Raras Setyoningrum; Prihandoko Prihandoko
JURTIK:Jurnal Penelitian dan Pengembangan Teknologi Informasi dan Komunikasi Vol 7 No 2 (2018): JURTIK : Jurnal Teknologi Informasi dan Komunikasi
Publisher : LPPM STMIK BANDUNG

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (297.468 KB)

Abstract

The use of information technology in the world of education has developed a lot, especially in universities. As an example of the application of college-level information technology is the delivery of information presented through the official website of the Higher Education. One of the web-based systems owned by the Sekolah Tinggi Teknologi Indonesia Tanjungpinang is the Final Semester Examination Information System (SIUAS). With this technology, the final semester exam no longer uses the manual method with answer sheets but students can answer directly on a computer or laptop. Satisfaction level analysis is important to know the extent of expectations and realities of system users in an effort to achieve the perfection of an information system and can meet user expectations. One method in analyzing user satisfaction is end user computing satisfaction (EUCS). Dimensions contained in EUCS consist of content, accuracy, format, ease of use and timeliness. This type of research is descriptive research that is intended to describe the phenomena that exist, which take place now or in the past. Data collection methods include observation, interviews and questionnaires with a sample of 47 respondents who are active users of SIUAS. The results of the analysis of the level of satisfaction of SIUAS users of the Tanjungpinang Indonesian Institute of Technology used the EUCS method of 87.01% with a gap of 12.99% meaning that the user is in a very satisfied category range. Of the five dimensions, the variable easy of use has the smallest gap, namely 9.9% while the biggest gap is in the variable timeliness, which is 17.52%.
PERBANDINGAN KINERJA ALGORITMA C4.5, NAÏVE BAYES, K-NEAREST NEIGHBOR, LOGISTIC REGRESSION, DAN SUPPORT VECTOR MACHINES UNTUK MENDETEKSI PENYAKIT KANKER PAYUDARA Taghfirul Azhima Yoga Siswa; Prihandoko Prihandoko
JURTIK:Jurnal Penelitian dan Pengembangan Teknologi Informasi dan Komunikasi Vol 7 No 2 (2018): JURTIK : Jurnal Teknologi Informasi dan Komunikasi
Publisher : LPPM STMIK BANDUNG

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (792.339 KB) | DOI: 10.58761/jurtikstmikbandung.v7i2.105

Abstract

Evaluate the best performance comparison of C4.5, Naïve Bayes, K-Nearest Neighbor, Logistic Regression, and Support Vector Machines classification methods for detecting breast cancer using a 10 fold Cross Validation test by comparing the values of accuracy, precision, and recall using confusion matrix . The breast cancer dataset used was 699 records with 11 indicator parameters consisting of Code Number, Clump Thickness, Uniformity of Cell Size, Uniformity of Cell Shape, Marginal Adhesion, Single Epithelial Cell Size, Bare Nuclei, Bland Chromatin, Normal Nucleoli, Mitoses, and Classes obtained from http://archive.ics.uci.edu. The data was processed using Rapid Miner Version 9 software. The results of this study found that the percentage of performance of each classification algorithm analyzed, that is C4.5 Algorithm (accuracy 93.70%, precision 94.26%, recall 87.86%), Naïve Bayes Algorithm (accuracy 96.19 %, precision 92.25%, recall 97.50%), K-Nearest Neighbor Algorithm (95.61% accuracy, precision 94.99%, recall of 92.43%), Logistic Regression Algorithm (accuracy 96.77%, precision 95.93%, recall 94.98%), and Support Vector Machines algorithm (accuracy 96.78%, precision 94.83%, recall 96.20%). The best performance results tested using T-Test found that the Logistic Regression and Support Vector Machines algorithm has the same highest accuracy value that is equal to 0.968.
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.
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 : LPPM 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.
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%.
Evaluasi Layanan Kesehatan Aplikasi Depok Single Window Dengan Metode System Usability Scale dan Heuristic Evaluation Amelinda Kusumaningtyas; Prihandoko Prihandoko
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 11 No 1: Februari 2024
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jtiik.20241117714

Abstract

Penerapan Smart City di Indonesia merupakan bentuk upaya pemerintah Indonesia dalam pengembangan dan pengelolaan daerah di era desentralisasi. Salah satu contoh implementasi ini adalah e-government di Kota Depok dengan meluncurkan aplikasi mobile bernama Depok Single Window (DSW). Aplikasi DSW merupakan aplikasi layanan publik yang dimiliki oleh Pemerintah Kota Depok untuk meningkatkan kualitas layanan publik memanfaatkan potensi teknologi telematika secara baik. Untuk meningkatkan kesejahteraan rakyat dan kepercayaan masyarakat terhadap keandalan aplikasi layanan publik, perlu diadakan evaluasi aplikasi. Untuk evaluasi ini maka digunakan 2 metode yaitu System Usability Scale (SUS) dan Heuristic Evaluation (HE).Data yang digunakan dalam Metode SUS merupakan data primer yang diperoleh langsung dari masyarakat Kota Depok melalui penyebaran kuesioner dengan Google Form. Data yang digunakan dalam Metode HE juga merupakan data primer yang diperoleh langsung dari para ahli yang memahami usability dari aplikasi mobile dan UI/UX dengan memberikan penilaian berdasarkan 10 prinsip heuristik Nielsen. Dalam Metode SUS didapatkan skor penilaian sebesar 68,75 yang menyatakan bahwa layanan kesehatan yang dievaluasi masih dibawah nilai minimal yang harus didapatkan. Sementara itu, evaluasi yang dilakukan dengan Metode HE menemukan total 13 masalah usability yang telah dievaluasi oleh para ahli. Masalah-masalah tersebut telah melanggar 8 dari 10 prinsip heuristik, yaitu Consistency and Standards, Visibility of System Status, User Control and Freedom, Aesthetic and Minimalist Design, Error Prevention, Recognize, Diagnose, and Recover from Errors, Recognition Rather Than Recall, dan Flexibility and Efficiency of Use.
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

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

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

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

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
Co-Authors A Benny Mutiara Ahmad Fajri Alyauma Hajjah Amelinda Kusumaningtyas Anita Muliawati Arie Wijaya Armaini Akhirson Asep Juarna Azim Zaliha binti Abd Aziz Bagus Satria Benny Mutiara Bertalya Bertalya Citra Handayani Citra Handayani Dadang Priyanto Deny Jollyta Deny Jollyta Dyah Mieta Setyawati 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 Sigit Widiyanto Sunny Arief Sudiro Syahifa Rahmita Irlianti Syaikhuddin, Muhammad Miftakhul Tagfirul Azima Yoga Siswa, Muspidar Mulyadi Tjahjanto Tjahjanto Widya C Widya Cholil Zaidiah, Ati