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All Journal JISTECH: Journal of Information Science and Technology Sistemasi: Jurnal Sistem Informasi JSiI (Jurnal Sistem Informasi) Building of Informatics, Technology and Science Jurasik (Jurnal Riset Sistem Informasi dan Teknik Informatika)
Inan, Dedi I
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Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Energy Engineering

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PREDICTING THE CHARACTERISTICS OF DRIVER’S LICENSE APPLICANTS AT SATPAS POLRESTA MANOKWARI USING HIERARCHICAL MULTIPLE REGRESSION Sahetapi, Merlyn Florensia; Inan, Dedi I; Sanglise, Marlinda; Juita, Ratna; Baisa, Lorna Y
Jurnal Sistem Informasi Vol 11 No 2 (2024)
Publisher : Universitas Serang Raya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30656/jsii.v11i2.9056

Abstract

This research aims to predict the characteristics of driver's license (SIM) applicants at the SATPAS (Driver’s License Issuance Unit) Polresta Manokwari using Hierarchical Multiple Regression analysis. The study explores six key variables: gender, age, occupation, city, type of driver's license, and type of application, as predictors of applicant characteristics. The analysis was conducted using SPSS version 29, with data collected from the population of driver's license applicants since 2023. Three models were tested, with Model 3 being identified as the best predictor, explaining 7% of the variance in applicant characteristics (R² = 0.070). This model incorporates the variables of age, occupation, city, and type of application, while gender and driver's license class were found to have no significant individual impact. The partial t-test results show that age, occupation, city, and type of application significantly influence applicant characteristics, with negative regression coefficients indicating that an increase in these variables leads to a decrease in the predicted characteristics of SIM applicants. The study highlights practical implications for SATPAS, suggesting that service processes could be improved by considering demographic factors such as age and occupation in order to optimize resource allocation and reduce service complexity. However, the study has several limitations. The use of secondary data limits the completeness and accuracy of the analysis, and the limited number of variables results in a narrow interpretation of the factors influencing SIM applicants. Additionally, the model explains only a small portion of the variance in applicant characteristics, suggesting that other unmeasured factors, such as education level or driving experience, may play a more significant role. Furthermore, the findings are not generalizable to other regions, as local conditions may impact license application patterns. Future research should address these limitations by collecting primary data, expanding the range of variables, employing more sophisticated analytical methods, and exploring other regions. This would provide a more comprehensive understanding of the factors affecting driver's license applicants and contribute to enhancing the quality of SIM issuance services in Indonesia. Keywords: Driver's License, Prediction, Hierarchical Multiple Regression, Applicant Characteristics
Dissatisfaction of a Mobile-Based Application from Different Platforms Using Naïve Bayes for Sentiment Analysis and LDA for Topic Modelling Ohoilulin, Anastasya; Inan, Dedi I; Juita, Ratna; Sanglise, Marlinda
Building of Informatics, Technology and Science (BITS) Vol 6 No 2 (2024): September 2024
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v6i2.5729

Abstract

A mobile application that is built and runs on different platforms, such as iOS (Apple App Store) and Android (Google Play Store), may not necessarily have the same user satisfaction (dissatisfaction) reviews understood by both user segments. This is due to, for example, the differences in the technology used, which ultimately result in different user behaviors. This can be observed from the average ratings on each platform, even though it is the same application. Therefore, this research aims to provide a foundation for the assumptions made. The case study used is the Satu Sehat mobile application, a widely utilized health service application. Text mining methods: sentiment analysis using Naive Bayes and topic modeling using Latent Dirichlet Allocation (LDA) were chosen due to their relevance to the research objectives. A total of 21,750 reviews from the Google Play Store and 7,350 reviews from the Apple App Store were collected using scraping techniques. The results showed that sentiment analysis model on negative sentiment in the Apple App Store excelled with a precision of 93%, recall of 93%, and F1-score of 95%, while in the Google Play Store it had a precision of 82%, recall of 87%, and F1-score of 85%. However, the performance of the positive sentiment model in the Apple App Store was very low, with a precision of 63%, recall of 33%, and F1-score of 43%, compared to the Google Play Store which had a precision of 78%, recall of 71%, and F1-score of 74%. This indicates that a higher level of dissatisfaction is observed in the Apple App Store compared to Android. These results are consistent with the average ratings of the application on both platforms. Topic modeling results, which presented 15 topics from each platform, showed similar common issues such as login, OTP verification, and data input errors on both platforms. However, reviews of the Satu Sehat running on the Apple tend to be more negative compared to the one of Android. Therefore, improving the application quality of the Apple platform is more expected to meet user expectations and enhancing the overall rating as in the Andrond one.
Apa Yang Memotivasi Seseorang Mengakses Aplikasi Mobile Laporkitong? Perspektif Teori Uses And Gratification (U&G) Dengan PLS-SEM Azizah, Putri W; Inan, Dedi I; Sanglise, Marlinda
Jurasik (Jurnal Riset Sistem Informasi dan Teknik Informatika) Vol 9, No 1 (2024): Edisi Februari
Publisher : STIKOM Tunas Bangsa Pematangsiantar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/jurasik.v9i1.745

Abstract

Government Agencies BAPPEDA In Province Of Western New Guinea Utilizes A Technology By Making A Mobile Application Laporkitong Which Useful To Inform Incompatibility Of Using Soil. The Youth Laporkitong Application And Lack Of Knowledge Regarding This Application Made A Problem In Use. So That, It Is Necessary To Held An Analysis Why Individual Wants To Access Laporkitong Application By Using Uses And Gratification (U&G) Theory In Order To Maximize Its Function. In This Study, It Is Found A Consented Hypothesis Namely H5, H7, H8, H9. While A Rejected Hypothesis H1, H2, H3, H4, H6, H10. From This Study It Can Be Concluded That Motivating People To Intent Of Using Laporkitong Application Are Exposure, Social Sharing, And Affection. Then, Towards Intention Of Continuing By Using Application Is Social Sharing Variable. Also, Things Which Need To Repair Is A Prove That Not Influent To Intention   Of Using Are Information Seeking, Entertainment And Intention Of Continuing Consume Are Information Seeking, Entertainment, Exposure, And Affection.
PREDICTING THE CHARACTERISTICS OF DRIVER’S LICENSE APPLICANTS AT SATPAS POLRESTA MANOKWARI USING HIERARCHICAL MULTIPLE REGRESSION Sahetapi, Merlyn Florensia; Inan, Dedi I; Sanglise, Marlinda; Juita, Ratna; Baisa, Lorna Y
Jurnal Sistem Informasi Vol 11 No 2 (2024)
Publisher : Universitas Serang Raya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30656/jsii.v11i2.9056

Abstract

This research aims to predict the characteristics of driver's license (SIM) applicants at the SATPAS (Driver’s License Issuance Unit) Polresta Manokwari using Hierarchical Multiple Regression analysis. The study explores six key variables: gender, age, occupation, city, type of driver's license, and type of application, as predictors of applicant characteristics. The analysis was conducted using SPSS version 29, with data collected from the population of driver's license applicants since 2023. Three models were tested, with Model 3 being identified as the best predictor, explaining 7% of the variance in applicant characteristics (R² = 0.070). This model incorporates the variables of age, occupation, city, and type of application, while gender and driver's license class were found to have no significant individual impact. The partial t-test results show that age, occupation, city, and type of application significantly influence applicant characteristics, with negative regression coefficients indicating that an increase in these variables leads to a decrease in the predicted characteristics of SIM applicants. The study highlights practical implications for SATPAS, suggesting that service processes could be improved by considering demographic factors such as age and occupation in order to optimize resource allocation and reduce service complexity. However, the study has several limitations. The use of secondary data limits the completeness and accuracy of the analysis, and the limited number of variables results in a narrow interpretation of the factors influencing SIM applicants. Additionally, the model explains only a small portion of the variance in applicant characteristics, suggesting that other unmeasured factors, such as education level or driving experience, may play a more significant role. Furthermore, the findings are not generalizable to other regions, as local conditions may impact license application patterns. Future research should address these limitations by collecting primary data, expanding the range of variables, employing more sophisticated analytical methods, and exploring other regions. This would provide a more comprehensive understanding of the factors affecting driver's license applicants and contribute to enhancing the quality of SIM issuance services in Indonesia. Keywords: Driver's License, Prediction, Hierarchical Multiple Regression, Applicant Characteristics
Does JASTIP as Facilitating Condition Affect the E-Marketplace Adoption in Developing Region? Sipahelut, Novelia; Inan, Dedi I; Juita, Ratna; Indra, Muhamad
Sistemasi: Jurnal Sistem Informasi Vol 14, No 6 (2025): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v14i6.5393

Abstract

E-marketplaces are expanding in developing regions but face barriers related to logistics costs and infrastructure. JASTIP, an informal proxy shopping service, has emerged as a potential solution to improve accessibility. This study examines the influence of perceived cost, social influence, hedonic motivation, and facilitating conditions on e-marketplace adoption using a modified UTAUT model analyzed through PLS-SEM with data from 185 respondents in West Papua. The findings show that hedonic motivation and social influence significantly increase user intention, perceived cost directly affects usage behavior, and facilitating conditions strengthen intention but do not directly influence usage. These results emphasize the role of JASTIP in reducing costs and improving accessibility. This study recommends improving JASTIP access and infrastructure, implementing flexible pricing policies, and promoting community-based marketing to enhance e-marketplace adoption in developing regions.
FAKTOR-FAKTOR YANG MENGHAMBAT PEMANFAATAN ADOPSI LAYANAN E-LEARNING DI UNIVERSITAS PAPUA : INNOVATION RESISTANCE THEORY (IRT): FACTORS THAT OBSTRUCT THE USE OF ADOPTION OF E-LEARNING IN UNIVERSITY OF PAPUA : INNOVATION RESISTANCE THEORY (IRT) Febriarman, Argi Dwi Raga; Inan, Dedi I; Paiki, Fridolin F
JISTECH: Journal of Information Science and Technology Vol 12 No 3 (2023): Volume 12 Nomor 3 Tahun 2023
Publisher : Universitas Papua

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30862/jistech.v12i3.349

Abstract

E-learning is an online learning service that can help and support the student lecture process. Since e-learning was first created at the University of Papua, until now there has been no specific research examining the adoption of e-learning utilization. Therefore, this study aims to determine the factors that hinder the adoption of the use of e-learning at the University of Papua using the IRT theory (Usage Barrier, Tradition Barrier, Perceived Cost Barrier) and combined with external variables Perceived Ease Of Use (PEOU) and Facilitating Conditions (FC) as measured by Structural Equation Modeling-Partial Least Square (SEM-PLS). Questionnaires were distributed to students using e-learning at the University of Papua. A total of 263 respondents were obtained in distributing questionnaires for almost two months. From the results of this study it was found that tradition barriers and perceived cost barriers were inhibiting factors for the adoption of e-learning utilization and had no positive effect, while usage barriers, perceived ease of use and facilitating conditions had a positive influence on e-learning adoption.
Co-Authors Azizah, Putri W Baisa, Lorna Y Febriarman, Argi Dwi Raga Indra, Muhamad Ohoilulin, Anastasya Paiki, Fridolin F Ratna Juita Sahetapi, Merlyn Florensia Sanglise, Marlinda Sipahelut, Novelia