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Jurnal Informatika Dan Tekonologi Komputer
Published by Pusat Riset dan Inovasi Nasional
ISSN : 28099249     EISSN : 28099230     DOI : https://doi.org/10.55606/jitek.v5i1
Core Subject : Science, Education, Engineering,
Computer Science & IT Control & Systems Engineering Other
Jurnal Informatika dan Teknologi Komputer (JITEK), dan P-ISSN:2809-9249 (Cetak) dan E-ISSN:2809-9230 (Online). Jurnal JITEK diterbitkan Pusat Riset dan Inovasi Nasional, terbit setahun Tiga kali (Maret, Juli dan November) menerapkan proses peer-review dalam memilih artikel berkualitas berdasarkan penelitian ilmiah dan teoritis.Jurnal ini terakreditasi SINTA 4 (Surat Keputusan Direktur Jenderal Pendidikan Tinggi, Riset, dan Teknologi Nomor 10/C/C3/DT.05.00/2025 tanggal 21 Maret 2025 tentang Peringkat Akreditasi Jurnal Ilmiah Periode I Tahun 2025) dimulai dari Volume 2 Nomor 2 Tahun 2022 sampai Volume 7 Nomor 1 Tahun 2027. JITEK diterbitkan untuk mengembangkan dan memperkaya diskusi ilmiah bagi para sarjana dan penulis yang menaruh minat pada isu-isu Teknologi dan penerapannya. Redaksi menerima artikel berbasis teori dan penelitian. Cakupan keilmuan Jurnal ini mengenai penelitian Rekayasa Sistem, Teknik Informatika/Teknologi Informasi, Manajemen Informatika dan Sistem Informasi.
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 166 Documents
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Pengembangan Video Pembelajaran Desain Interaksi Berbasis Microlearning untuk Mahasiswa Jurusan Sistem Informasi Li Cen; Jenny Tan; Tony Tan
Jurnal Informatika Dan Tekonologi Komputer (JITEK) Vol. 6 No. 1 (2026): Maret : Jurnal Informatika dan Tekonologi Komputer
Publisher : Pusat Riset dan Inovasi Nasional

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55606/jitek.v6i1.9494

Abstract

The rapid development of digital technology demands more efficient learning strategies, particularly for Information Systems students who often struggle to understand material delivered through conventional methods. This study aims to design and evaluate microlearning-based instructional videos on interaction design with a duration of less than two minutes to enhance student comprehension. The Multimedia Development Life Cycle (MDLC) framework was employed, consisting of six stages: concept, design, material collecting, assembly, testing, and distribution. Data were obtained through interviews, expert validation, student assessments, and a Paired Sample T-test administered to 10 Information Systems students using pre-test and post-test instruments. Expert validation produced a feasibility score of 3.6, while students rated the videos at 4.4, indicating that the material was perceived as engaging, clear, and easy to understand. The T-test results showed a Sig. (2-tailed) value of 0.017 (< 0.05), confirming a significant difference between pre-test and post-test scores. These findings demonstrate that ultra-short microlearning videos effectively improve students’ understanding of interaction design concepts. Furthermore, this study contributes to the existing literature by addressing the research gap regarding the effectiveness of ultra-micro content in the context of interaction design learning.
Implementasi Sistem Informasi Penjualan Berbasis Web Menggunakan Framework Laravel 12 : Studi Kasus Pada Toko Ulkids Pegandon Shofa Arinal Hakikiyah; Bagus Sudirman; Ahmad Ashifuddin Aqham; Dendy Kurniawan; Moh. Muthohir
Jurnal Informatika Dan Tekonologi Komputer (JITEK) Vol. 6 No. 1 (2026): Maret : Jurnal Informatika dan Tekonologi Komputer
Publisher : Pusat Riset dan Inovasi Nasional

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55606/jitek.v6i1.9573

Abstract

This study, titled “Implementation of a Web-Based Sales Information System Using the Laravel 12 Framework: A Case Study at Ulkids Store Pegandon,” was conducted to address declining sales performance caused by conventional sales processes and manual transaction management. Ulkids Store experienced a 62.6% decrease in profit from 2019 to 2025, mainly due to limited promotional reach, inaccurate stock recording, and inefficient sales transactions. The research aims to design and implement a web-based sales information system to improve transaction efficiency, data accuracy, and customer accessibility. A qualitative approach with a case study method was applied, while system development followed the Waterfall model. The system was built using Laravel 12, PHP, MySQL, and a Bootstrap 5 user interface, and was tested using Black Box Testing to ensure all functions operated according to user requirements.
Agile-Scrum and Business Model Canvas in it Project Management: Integration, Effectiveness, and Critical Success Factors - Systematic Literature Review Kahpi Baiquni Arifani; Dody Pintarko; Anggraini Puspita Sari
Jurnal Informatika Dan Tekonologi Komputer (JITEK) Vol. 6 No. 1 (2026): Maret : Jurnal Informatika dan Tekonologi Komputer
Publisher : Pusat Riset dan Inovasi Nasional

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55606/jitek.v6i1.10070

Abstract

A Information systems (IS)/information technology (IT) project management is a critical aspect of successful technology implementation in organizations. Agile and Scrum methodologies have been proven to improve software development effectiveness, while Business Model Canvas (BMC) helps organizations design innovative and sustainable business models. This Systematic Literature Review (SLR) aims to identify, analyze, and synthesize findings from various empirical studies on the application of Agile, Scrum, and Business Model Canvas in IS/IT project management, and explore the integration of these two approaches. This study uses the PRISMA 2020 protocol with an analysis of 95 journals published between 2020 and 2025. Search databases include Google Scholar, SCOPUS, and local institutional repositories. Inclusion criteria include Indonesian/English language studies with a focus on Agile-Scrum (58 journals) and Business Model Canvas (37 journals). The analysis shows that: (1) Agile-Scrum implementation increases development efficiency by 30-50% with a 61.1% (58/95) increase in team adaptability; (2) Business Model Canvas is effective in business strategy with 38.9% (37/95) adoption in empirical studies; (3) Agile-Scrum and BMC integration is found in 15.8% of journals with more positive results (average score 9.3/10 vs 7.2 for Agile-only); (4) Critical Success Factors include organizational leadership (85%), team capability (82%), and stakeholder involvement (75%). The integration of Agile-Scrum with Business Model Canvas results in a holistic approach that combines operational and strategic aspects, increasing the probability of IS/IT project success by up to 78% compared to a single approach. However, implementation still faces challenges related to organizational culture changes, resource availability, and long-term impact evaluation.
Identifikasi Citra Penyakit Monkeypox dengan Random Forest Serta Ekstraksi Fitur VGG19: Indonesia Muhammad Azka Zaki; Eka Prakarsa Mandyartha; Achmad Junaidi
Jurnal Informatika Dan Tekonologi Komputer (JITEK) Vol. 6 No. 1 (2026): Maret : Jurnal Informatika dan Tekonologi Komputer
Publisher : Pusat Riset dan Inovasi Nasional

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55606/jitek.v6i1.10132

Abstract

Monkeypox is an infectious disease that can be recognized through images of the patient's skin lesions. A fast and accurate diagnosis method is required to identify Monkeypox. This research aims to identify Monkeypox imagery using the VGG19 feature extraction method, which is then classified using the Random Forest algorithm. The dataset consists of 770 original images, which were expanded to 5,860 images through geometric transformation augmentation. The test results show that the VGG19 feature extraction method with Random Forest classification achieved an accuracy of 95.1%, indicating good performance. This finding suggests the potential of this method as a machine learning approach for detecting Monkeypox and can be further developed with other artificial intelligence approaches.
Analisis Blockchain untuk Otentikasi Transaksi pada Sistem Pembayaran Digital Viorella Natalya Simanjuntak; Joitanata Saragi; Aditya Rizqy Bakhtiar; Vuza Roinda Br Sihombing; Zalsa Nabilla Rosa Dalimunthe; Yohana Adelia Nugroho; Wilfy Hardi Napitupulu; Sudarto Sudarto
Jurnal Informatika Dan Tekonologi Komputer (JITEK) Vol. 6 No. 1 (2026): Maret : Jurnal Informatika dan Tekonologi Komputer
Publisher : Pusat Riset dan Inovasi Nasional

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55606/jitek.v6i1.10445

Abstract

This study aims to analyze the implementation of blockchain technology in enhancing the security and authentication of transactions in digital payment systems. The method employed is a Systematic Literature Review (SLR) of 21 accredited national and international journals published between 2020 and 2025. The SLR process involved the stages of identification, selection, quality assessment, and synthesis of literature relevant to the research topic. The results indicate that blockchain technology is capable of improving the security, transparency, and efficiency of digital payment systems through decentralized and tamper-resistant transaction recording mechanisms. The application of smart contracts has proven effective in verifying transaction authenticity without the involvement of third parties, while simultaneously reducing operational costs. Nevertheless, challenges such as inadequate regulatory frameworks, limited infrastructure, and low levels of digital literacy remain significant barriers to the widespread adoption of blockchain in Indonesia. In conclusion, blockchain has the potential to serve as a critical foundation for the development of a secure and trustworthy digital payment ecosystem, with opportunities for future research focusing on the integration of Artificial Intelligence (AI) and the Internet of Things (IoT) to further enhance the efficiency and reliability of digital payment systems.
The Perancangan Sistem Penyiram Tanaman Otomatis Berbasis IoT di Sekolah Alam Purwakarta Faradilla Amanda Zalfa; Ahmad Anas; Rahmat Supriyatna
Jurnal Informatika Dan Tekonologi Komputer (JITEK) Vol. 6 No. 1 (2026): Maret : Jurnal Informatika dan Tekonologi Komputer
Publisher : Pusat Riset dan Inovasi Nasional

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55606/jitek.v6i1.10492

Abstract

The application of the Internet of Things (IoT) in agriculture (Smart Farming) has proven to be crucial for increasing productivity, efficiency, and crop quality. This study focuses on the design and development of an IoT-based automatic plant irrigation system as a solution to overcome the challenges of imprecise manual irrigation at Sekolah Alam Purwakarta. This system aims to optimize water allocation and make it easier for workers to control plant care in real-time through an IoT-based application. The method used is Research and Development (R&D) with the ADDIE model approach. The results show that the de-veloped system is capable of automatically activating irrigation based on soil moisture readings (with a threshold of 50% RH), while displaying real-time data for control. This allows workers to know the exact water requirements and effectively prevent drought or overwatering conditions. Overall, the design of this system provides an effective solution for water conservation and work efficiency in supporting plant care at Sekolah Alam Purwakarta.
Rancang Bangun Sistem Pemasaran Digital dan Rekomendasi Produk Kerajinan Kulit Berbasis Android: (Studi Kasus CV Prima Semesta Alam) Isfa Fadil Muhammad; Afina Lina Nurlaili; Budi Mukhamad Mulyo
Jurnal Informatika Dan Tekonologi Komputer (JITEK) Vol. 6 No. 1 (2026): Maret : Jurnal Informatika dan Tekonologi Komputer
Publisher : Pusat Riset dan Inovasi Nasional

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55606/jitek.v6i1.10537

Abstract

CV Prima Semesta Alam, an SME producing reptile leather crafts in Surabaya, faces challenges in expanding its market reach due to conventional marketing processes and unstructured stock management. General e-commerce platforms often fail to highlight the uniqueness of specific products like exotic leather, making it difficult for consumers to find relevant items. This study aims to build an Android-based digital marketing system integrated with a smart product recommendation feature using Term Frequency-Inverse Document Frequency (TF-IDF) and Cosine Similarity algorithms. The system was developed with a client-server architecture using Kotlin for the user interface and Golang as the backend. System testing showed that the recommendation algorithm was able to provide relevant product suggestions based on descriptive attribute similarities (material, color, function), with logic validation results demonstrating full consistency between manual calculations and system outputs. Usability evaluation using the System Usability Scale (SUS) yielded an average score of 76.16, placing the application in the Acceptable category with a Good rating.
Analisis Perbandingan Metode K-Means dan Gaussian Mixture Model dalam Pengelompokan Playlist Musik Berbasis Fitur Audio Daniel Prasetiyo Dodi Darmawan; Christopher Mathew Putra; Samuel Yahya; Darren Jusman; Alfi Syahrian; Vitri Tundjungsari
Jurnal Informatika Dan Tekonologi Komputer (JITEK) Vol. 6 No. 1 (2026): Maret : Jurnal Informatika dan Tekonologi Komputer
Publisher : Pusat Riset dan Inovasi Nasional

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55606/jitek.v6i1.10754

Abstract

Music streaming platforms rely on playlists as medium for users to store musical preferences and receive music recommendations based on the music stored. However, representing playlists as meaningful groups remains a major challenge due to the high diversity characteristics of music. In addition, the distribution of musical characteristics within playlists can vary significantly. This study aims to compare two clustering models with different approaches hard clustering using the K-Means method and soft clustering using the Gaussian Mixture Model (GMM). Playlists are represented as statistical aggregations of audio feature data from songs, such as energy, acousticness, and danceability. The hard clustering approach using K-Means produces compact and clearly separated clusters, while the Gaussian Mixture Model (GMM) generates clusters that capture playlist ambiguity, resulting in overlapping clusters due to its probabilistic nature. These differences have a direct impact on the implementation of the clustering results in downstream applications. This study emphasizes the importance of selecting an appropriate clustering method for further implementations, such as music recommendation systems, and provides insights into the trade-offs between interpretability and flexibility offered by both models.
Evaluating User Experience of the Workout Tracker Feature in the FIT HUB Mobile Fitness Application Using the User Experience Questionnaire and Usability Testing Khansa Sulthanah Rumi; Agustin Rusiana Sari
Jurnal Informatika Dan Tekonologi Komputer (JITEK) Vol. 6 No. 1 (2026): Maret : Jurnal Informatika dan Tekonologi Komputer
Publisher : Pusat Riset dan Inovasi Nasional

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55606/jitek.v6i1.10889

Abstract

The rapid development of digital fitness applications in Indonesia has been driven by increasing public awareness of healthy lifestyles. FIT HUB is a digital fitness application that provides a Workout Tracker feature to help users record and monitor their exercise activities. However, user reviews indicate that several issues remain, particularly related to feature clarity, navigation, and system stability. This study aims to evaluate the user experience of the FIT HUB application and identify areas for improvement to enhance user satisfaction and sustained application usage. This study employs a mixed-method approach by combining the User Experience Questionnaire (UEQ) and usability testing methods. Data were collected through the distribution of UEQ questionnaires to application users and the implementation of usability testing to identify usability issues encountered during task completion. The analysis results were used as a reference for formulating interface design improvement recommendations. The results of the UEQ evaluation indicate that the FIT HUB application demonstrates strong performance in terms of attractiveness and stimulation. However, aspects related to efficiency and novelty still require improvement. Furthermore, usability testing revealed several obstacles, particularly in accessing video tutorial features and storing exercise data. Based on these findings, an improved interface prototype was designed with a focus on enhancing feature clarity, navigation usability, and motivational elements. This study concludes that the combination of UEQ and usability testing methods is effective in identifying user experience issues and generating relevant interface design improvement recommendations for digital fitness applications
Comparison of Deep Learning Models for Sentiment Analysis of IPOT Financial App Reviews Using Convolutional Neural Network (CNN) and IndoBERT I Gusti Ngurah Agung Pawana; Made Widya Jayantari; I Gusti Ngurah Agung Bagus Aryawana
Jurnal Informatika Dan Tekonologi Komputer (JITEK) Vol. 6 No. 1 (2026): Maret : Jurnal Informatika dan Tekonologi Komputer
Publisher : Pusat Riset dan Inovasi Nasional

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55606/jitek.v6i1.10901

Abstract

The rapid expansion of mobile-based financial applications has generated a large volume of user reviews that contain valuable insights into user satisfaction and system performance. IPOT (Indo Premier Online Trading) is a widely used financial application in Indonesia, making sentiment analysis of its user reviews essential for evaluating its service and improving it. This study applies an experimental methodology to compare the performance of two deep learning architectures, Convolutional Neural Network (CNN) and IndoBERT, for sentiment analysis of financial application reviews. User review data were collected from the Google Play Store. Sentiment labels were automatically assigned based on user ratings, and the dataset was balanced using stratified sampling to obtain 15,000 reviews. Text preprocessing included case folding, removal of punctuation and special characters, tokenization, stopword removal, and stemming. The dataset was then split into training, validation, and testing sets, with oversampling applied only to the training data to prevent data leakage. The comparison between Convolutional Neural Networks (CNNs) and IndoBERT for sentiment analysis of IPOT financial application reviews shows that both models perform sentiment classification effectively, with different strengths across sentiment categories. The CNN model achieved higher overall accuracy (0.8113) compared to IndoBERT (0.7880), indicating strong performance in detecting dominant sentiment patterns, particularly positive sentiment. Meanwhile, IndoBERT achieved superior performance in negative and neutral sentiment classification, as evidenced by higher recall and F1 scores. The confusion matrix and error analysis results further indicate that IndoBERT is more effective at understanding contextual and nuanced language, whereas CNN is more sensitive to explicit lexical sentiment indicators.

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