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All Journal Jurnal IPTEK-KOM (Jurnal Ilmu Pengetahuan dan Teknologi Komunikasi) Proceeding of the Electrical Engineering Computer Science and Informatics Masyarakat Telematika Dan Informasi : Jurnal Penelitian Teknologi Informasi dan Komunikasi MIND (Multimedia Artificial Intelligent Networking Database) Journal Jurnal Informatika Terpadu DBESTI: Journal of Digital Business and Technology Innovation
Prima, Pudy
Sekolah Tinggi Teknologi Terpadu Nurul Fikri
Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Languange, Linguistic, Communication & Media Library & Information Science Mathematics Social Sciences

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IMPROVEMENT STRATEGY FOR OPEN GOVERNMENT DATA USING FUZZY AHP: CASE STUDY JAKARTA OPEN DATA Pradipta, Dhea Junestya; Ariani, Septi; Sensuse, Dana Indra; Lusa, Sofian; Prima, Pudy
Masyarakat Telematika Dan Informasi : Jurnal Penelitian Teknologi Informasi dan Komunikasi Vol 11, No 1 (2020): Masyarakat Telematika Dan Informasi : Jurnal Penelitian Teknologi Informasi dan
Publisher : Kementerian Komunikasi dan Informatika R.I.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.17933/mti.v11i1.166

Abstract

Open Government Data (OGD) implementation provides benefits for government performance and public services. Based on the Indonesian government's openness action plan 2018-2020, the importance of monitoring and evaluation of OGD implementation for sustainable development is emphasized. This study aims to prioritize criteria and provide recommendations for OGD evaluations at the Jakarta Open Data. Through the mix method approach, expert interviews have been conducted to test the validity of the criteria which then carried out the distribution of questionnaires to eleven expert respondents from five departments. The data is processed using the fuzzy-Analytic Hierarchy Process (AHP) to determine each weight on twenty criteria in four dimensions. The results of this study indicate that in the short term, OGD internal evaluations in the DKI Jakarta provincial government can be done by assessing eight main priority criteria, namely accuracy, completeness, compliance, understandability, timeliness, openness, functionality, and reliability which are then based on overall criteria. These results are the basis for discussion in the Data Forum and the establishment of Standard Operating Procedure (SOP) to assist and accelerate the process of collecting, processing, verifying and validating data from 51 regional work units. Externally, the Jakarta Open Data team can pay attention to the conditions of citizen engagement in the OGD and the existence of a Memorandum of Understanding (MoU) between relevant ministries or agencies that does not yet have data officers or information and documentation management officers for effective and efficient data processing
Factors Influencing e-Government Adoption (A Case Study of Information System Adoption in PPATK) (Faktor-faktor yang Memengaruhi Adopsi e-Government (Studi Kasus Adopsi Sistem Informasi di PPATK)) Syarifah Hanum; Rabiah Al Adawiyah; Dana Indra Sensuse; Jonathan Sofian Lusa; Assaf Arief; Pudy Prima
IPTEK-KOM : Jurnal Ilmu Pengetahuan dan Teknologi Komunikasi Vol 22, No 1 (2020): Jurnal IPTEK-KOM (Jurnal Ilmu Pengetahuan dan Teknologi Komunikasi)
Publisher : BPSDMP KOMNFO Yogyakarta, Kementerian Komunikasi dan Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33164/iptekkom.22.1.2020.19-30

Abstract

Information Communication Technology (ICT) has encouraged e-government implementation to improve government institution performance in society, business and fellow government institutions. Many Information Systems (IS) are created and used, but not all of them are successful. This is contrary to the expectations that were mentioned earlier. Therefore, it is necessary to conduct research to identify factors that influence e- government adoption. This paper presents a case study of information system adoption in a government institution. The study was conducted using TOE methods. Data were collected using qualitative method and subsequently processed using Qualitative Data Analysis tools. Results indicated that all factors analyzed in TOE framework influenced IS adoption. However, not all factors were properly conducted. Therefore, they need to be developed to address difficulties in IS adoption so it will meet the e-government implementation expectation.
Knowledge Management Maturity Assessment in Air Drilling Associates using G-KMMM Dana I. Sensuse; Richard Vinc; Ricky Nauvaldy Ruliputra; Siti Hadjar; Jonathan Sofian Lusa; Pudy Prima
Proceeding of the Electrical Engineering Computer Science and Informatics Vol 5: EECSI 2018
Publisher : IAES Indonesia Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (261.243 KB) | DOI: 10.11591/eecsi.v5.1694

Abstract

Since the early 1990s, companies in the oil & gas industry have realized that their business operations are knowledge-based, where company performance can be derived from faster identification, an assessment of an opportunity, and the speed of an exploitation. The oil & gas industry is one of the leading industries in the application and development of knowledge management; this is caused by changes in market and technology from 1990s to the beginning of the 21st century. Utilizing knowledge management is a must to be able to compete with other oil and gas industry companies. Currently, Air Drilling Associates (ADA) as one of the companies in oil & gas industry already has implemented knowledge management system, but its benefits are far from the expectation. In order to position their efforts and initialize knowledge management, companies need framework to use as a template. The objective of this paper is to measure the knowledge management maturity of Air Drilling Associates and other suggestion related to knowledge management for improvement.
Measuring Knowledge Management Readiness of Indonesia Ministry of Trade Dana Indra Sensuse; Jani Richi R. Siregar; Ronny Ansis; Jonathan Sofian Lusa; Pudy Prima
Proceeding of the Electrical Engineering Computer Science and Informatics Vol 5: EECSI 2018
Publisher : IAES Indonesia Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (339.784 KB) | DOI: 10.11591/eecsi.v5.1698

Abstract

Knowledge is one of the important assets for organization. Managing knowledge properly will enable the organization to achieve its objectives effectively and efficiently. Since risk of failed implementation of Knowledge Management (KM) might occur, organization needs to measure their KM Readiness beforehand to successfully implement KM. This study is intended to measure KM Readiness in government agency, namely Directorate of Bilateral Negotiations in Ministry of Trade. The research model for measuring KM readiness was developed based on previous relevant studies. KM enablers, individual acceptance, and KM SECI processes were used to develop the model and research instruments. KM Readiness in government agency was measured by accommodating factor analysis in research model. Data were collected from 53 employees as valid samples. The result shows that KM Readiness level of the Directorate of Bilateral Negotiations in Ministry of Trade is "ready but needs a few improvement".
Pengembangan Sistem Deteksi Tuberkulosis pada Citra X-Ray Menggunakan Metode Convolutional Neural Network (CNN) dengan Framework Laravel Alimi, Aldi Akbar; Adriansyah, Ahmad Rio; Prima, Pudy
Jurnal Informatika Terpadu Vol 10 No 2 (2024): September, 2024
Publisher : LPPM STT Terpadu Nurul Fikri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54914/jit.v10i2.1437

Abstract

Tuberculosis or TB is a disease caused by Mycobacterium Tuberculosis, which has a high transmission level. TB disease can be diagnosed through several methods, namely, using sputum samples and using x-ray scans. However, both methods take a long time to detect. Therefore, a detection system is needed to detect TB disease quickly and can be done by anyone. This research creates a detection system that can detect TB disease through chest x-ray images. The detection system is a web-based application built using the Laravel framework and a machine learning model with the Convolutional Neural Network (CNN) method for X-ray image analysis. This research will apply the CNN model that has been made into a web-based application through an API created using the FastAPI framework. The results of research on the detection system show that the detection system can detect TB disease. Proven by the results of testing conducted using the black box testing method, the test results show that the test success rate is 87%. In addition, the machine learning model with the CNN method can also provide classification on x-ray images well, where an accuracy of 93% is obtained on training data and 85% on test data.
Perancangan Prototipe Aplikasi Transportasi Umum Jabodetabek Berbasis Mobile dengan Pendekatan User-Centered Design Cahaya Arzeti; Pudy Prima; Reza Maulana
DBESTI: Journal of Digital Business and Technology Innovation Vol 3 No 1 (2026): Mei, 2026
Publisher : LPPM STT Terpadu Nurul Fikri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54914/dbesti.v3i1.2251

Abstract

The problem of congestion and low public interest in public transportation in Jabodetabek is caused by the lack of integration between modes, as well as limited access to information and payment systems. This research aims to design the user interface (UI) and user experience (UX) of a mobile application that integrates various modes of public transportation using the User-Centered Design (UCD) approach. Data was collected through surveys, interviews, and observations. The prototype was designed in Figma and tested in Maze using the System Usability Scale (SUS) method. The evaluation results showed an average SUS score of 82.7, which indicates a good level of usability. This research resulted in an intuitive application prototype that supports a more efficient user experience and contributes to the design of public transportation systems based on user needs. The findings are expected to encourage increased adoption of public transportation in Jabodetabek.
Prediksi Retensi Mahasiswa Menggunakan Algoritma Random Forest dengan Optimasi Algoritma Genetika PUDY PRIMA; AHMAD RIO ADRIANSYAH; ALFIAN NUR USYAID
MIND (Multimedia Artificial Intelligent Networking Database) Journal Vol 11, No 1 (2026): MIND Journal
Publisher : Institut Teknologi Nasional Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26760/mindjournal.v11i1.115-126

Abstract

AbstrakKetidakseimbangan kelas (imbalanced data) memicu bias mayoritas pada model konvensional dalam memprediksi retensi mahasiswa. Penelitian ini mengusulkan model peringatan dini (early warning system) dengan mengintegrasikan teknik penyeimbangan data Synthetic Minority Over-sampling Technique (SMOTE) dan pengklasifikasi Random Forest (RF). Untuk menghindari inefisiensi pencarian hyperparameter manual, Algoritma Genetika (GA) diaplikasikan guna melakukan optimasi secara global. Pengujian terhadap dataset historis mahasiswa STT Terpadu Nurul Fikri angkatan 2021 membuktikan bahwa kombinasi SMOTE dan GA-RF sangat efektif. Model hibrida ini mencapai akurasi global 99%, dengan nilai Precision 1,00 dan Recall 0,67 pada deteksi kelas minoritas (dropout). Analisis ekstraksi fitur (Feature Importance) mengungkap bahwa ketahanan studi mahasiswa didominasi oleh performa Indeks Prestasi Semester (IPS) di tahun pertama serta faktor administratif berupa jalur pendaftaran seleksi mandiri.Kata kunci: prediksi Dropout, Ketidakseimbangan Data, SMOTE, Random Forest, Algoritma GenetikaAbstractClass imbalance triggers majority bias in conventional models for predicting student retention. This study proposes an early warning model integrating the Synthetic Minority Over-sampling Technique (SMOTE) for data balancing and a Random Forest (RF) classifier. To avoid manual hyperparameter tuning inefficiencies, a Genetic Algorithm (GA) is applied for global optimization. Testing on the 2021 historical student dataset of STT Terpadu Nurul Fikri proves the effectiveness of combining SMOTE and GA-RF. The hybrid model achieved 99% global accuracy, with 1.00 Precision and 0.67 Recall in minority class (dropout) detection. Feature Importance analysis reveals that student study retention is predominantly driven by first-year Grade Point Average (GPA) performance and administrative factors, specifically the independent selection admission path.Keywords:  Dropout Prediction, Imbalanced Data, SMOTE, Random Forest, Genetic Algorithm
Co-Authors adriansyah, ahmad rio AHMAD RIO ADRIANSYAH ALFIAN NUR USYAID Alimi, Aldi Akbar Ansis, Ronny Ariani, Septi Assaf Arief Cahaya Arzeti Dana I. Sensuse Dana Indra Sensuse Hadjar, Siti Jani Richi R. Siregar Jonathan Sofian Lusa Jonathan Sofian Lusa Jonathan Sofian Lusa Lusa, Sofian Pradipta, Dhea Junestya Rabiah Al Adawiyah Reza Maulana Richard Vinc Richi R. Siregar, Jani Ricky Nauvaldy Ruliputra Ronny Ansis Ruliputra, Ricky Nauvaldy Sensuse, Dana I. Siti Hadjar Syarifah Hanum Vinc, Richard