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INDONESIA
Jurnal Teknologi Informatika dan Komputer
Published by Universitas Mohammad Husni Thamrin
ISSN : 26569957     EISSN : 26228475     DOI : https://doi.org/10.37012/jtik
Core Subject : Science, Engineering,
Computer Science & IT Electrical & Electronics Engineering
Jurnal Teknologi Informatika dan Komputer merupakan salah satu jurnal berbasis Open Journal System (OJS) yang dikelola oleh Lembaga Penelitian dan Pengabdian kepada Masyarakat (LPPM) Universitas Mohammad Husni Thamrin (UMHT) yang berisi artikel-artikel dengan topik Teknologi Informasi yang menampung karya ilmiah para dosen Perguruan Tinggi di Indonesia. Diharapkan jurnal ini mampu memberikan motivasi dan kontribusi ilmiah bagi perkembangan ilmu pengetahuan dan teknologi.
Arjuna Subject : Ilmu Komputer (semua kategori) - Aplikasi Ilmu Komputer
Articles 757 Documents
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Data Mining Analysis of Student Perceptions of ChatGPT Usage Using the C4.5 Algorithm Alifia Maulani; Prastyadi Wibawa Rahayu; I Made Dwi Ardiada
Jurnal Teknologi Informatika dan Komputer Vol. 12 No. 1 (2026): Jurnal Teknologi Informatika dan Komputer
Publisher : Universitas Mohammad Husni Thamrin

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37012/jtik.v12i1.3484

Abstract

The rapid advancement of artificial intelligence, particularly ChatGPT, has brought considerable changes to the educational landscape. Despite its benefits, concerns have emerged regarding its potential negative impact on students' critical thinking skills and learning independence. This study aims to classify the perceptions of students at Dhyana Pura University toward the use of ChatGPT in education and to measure the accuracy of the resulting classification model. The method applied is data mining using the Knowledge Discovery in Databases (KDD) approach with the C4.5 algorithm. Data were collected from 350 active students through a Likert scale-based questionnaire covering five indicators: ease of use, perceived benefits, impact on critical thinking, impact on learning independence, and attitude toward ChatGPT usage. The classification process was carried out using Altair AI Studio with an 80:20 training and testing data split. The results indicate that the majority of students hold a neutral perception toward the use of ChatGPT in academic activities. The classification model achieved an accuracy rate of 86.96%, which is categorized as good. This research is expected to serve as a reference for educational institutions in formulating policies on the responsible use of AI in academic settings.
Development of Native Android-based Attendance Application using MVVM Architecture Abdul Aziz; Sulistyo Puspitodjati
Jurnal Teknologi Informatika dan Komputer Vol. 12 No. 1 (2026): Jurnal Teknologi Informatika dan Komputer
Publisher : Universitas Mohammad Husni Thamrin

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37012/jtik.v12i1.3507

Abstract

WebView-based attendance application in government agencies experiences performance issues (startup time 2.33 seconds, memory 186 MB, success rate 82%) and weak verification. Previous research on face recognition still used monolithic architecture (verification time 5-7 seconds) and location validation only with fixed radius without distinguishing WFO/WFA scenarios. This study aims to develop the application to Android Native with MVVM, integrate client-server face recognition and Haversine for WFO/WFA, and measure performance improvement. The method uses the Waterfall model with one group pretest-posttest design. Testing of 7 metrics with 30 samples per metric using ADB, Logcat, and observation, as well as paired t-test (α=0.05). MVVM implementation was successful (UI conformity 98.4%, layer separation 100%). Client-side face detection achieved 91.7% accuracy with 1.7 seconds (64-74% faster). Client-server communication was efficient (compression 92.2%, total verification 1.81 seconds). Server verification achieved 96% accuracy with 100% specificity. Haversine validation achieved 0.21 m error (first data). Significant performance improvement (p<0.001): startup decreased by 33.5% (2.33→1.55 sec), login decreased by 48.0% (2.50→1.30 sec), attendance decreased by 23.3% (3.60→2.76 sec), memory decreased by 27.9% (186→134 MB), success rate increased to 94% (from 82%). The development of the application to Android Native with MVVM, integration of client-server face recognition (1.81 sec, 96%), and Haversine validation (0.21 m) succeeded in improving the performance and reliability of the attendance application significantly and measurably, as well as answering the five research gaps.
K-Means Analysis of West Java Coastal Capture Fish Production to Increase Fisheries Productivity Siti Salwa Azzahra; Fatia Amalia Maresti; Kiki Mustaqim
Jurnal Teknologi Informatika dan Komputer Vol. 12 No. 1 (2026): Jurnal Teknologi Informatika dan Komputer
Publisher : Universitas Mohammad Husni Thamrin

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37012/jtik.v12i1.3508

Abstract

Marine capture fisheries in West Java play a vital role in regional economic development, yet disparities in productivity among coastal areas remain unresolved. This study aims to classify coastal districts and cities in West Java based on key production indicators: fish catch volume, number of vessels, number of marine fishers, average weighted price per kilogram, and the number of fishing households. A K-Means Clustering approach was applied to group these regions into homogeneous clusters, enabling more targeted policy development. The analysis identified three distinct clusters: moderately productive areas with low efficiency, economically efficient areas with high-value catch but limited volume, and a dominant production zone with significantly higher outputs than others. These findings provide a clearer understanding of regional fishery dynamics and offer recommendations for each cluster. Proposed strategies include expanding infrastructure and training for low-performing regions, and focusing on product diversification, post-harvest processing, and sustainable management in high-performing areas. This study demonstrates the usefulness of data-driven methods to support marine fishery development and regional planning.
Literature Review on the Development of an AI-Based Humanoid Chatbot for UII IT Centrum Services Rafa Radhitya Riyanto; Rahadian Kurniawan
Jurnal Teknologi Informatika dan Komputer Vol. 12 No. 1 (2026): Jurnal Teknologi Informatika dan Komputer
Publisher : Universitas Mohammad Husni Thamrin

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37012/jtik.v12i1.3535

Abstract

In this literature review, chatbots and their use in digital marketing and promotion have been thoroughly studied in 2024 to 2026. In a literature review of 20 main references, chatbot technology has evolved from service-driven systems to humanistic ones with help from humanoid chatbots and anthropomorphic AI. In 2024 starting from reactive automation to macro digital transformation, then into the public sector and in 2026 ended with emotional closeness. Large Language Models (LLM) technologies such as GPT and multimodal artificial intelligence are the key platforms to make the interaction more natural and contextually sensitive. The review concludes that chatbots are not simply automation tools but are key strategic factors for the digital economy ecosystem to improve efficiency, build customer relationships and drive promotion for the consumer. The literature review is also used as the main theoretical foundation in the author’s thesis to design and expand the first AI-based chatbot system for the UII IT Centrum information services.
Implementation of the BiLSTM Model for Detecting AI-Generated Indonesian Text Rafil Moehamad Alif; Syariful Alam; Chandra Dewi Lestari
Jurnal Teknologi Informatika dan Komputer Vol. 12 No. 1 (2026): Jurnal Teknologi Informatika dan Komputer
Publisher : Universitas Mohammad Husni Thamrin

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37012/jtik.v12i1.3551

Abstract

The rapid advancement of generative Artificial Intelligence (AI) presents challenges to academic integrity due to potential misuse like plagiarism. This study develops a text detection system specifically for the Indonesian language using a Deep Learning approach with a Bidirectional Long Short-Term Memory (Bi-LSTM) architecture. The research methodology follows the Cross-Industry Standard Process for Data Mining (CRISP-DM) framework. A dataset comprising 5,008 text rows was compiled via web scraping from journalism platforms and academic journals indexed in SINTA 4 for human-written texts, while AI-generated counterparts were engineered using ChatGPT and Google Gemini paraphrases. Text features were extracted using a Keras Tokenizer and Embedding Layer with 64 dimensions. Evaluation of the trained Bi-LSTM model on a 30% validation split demonstrated an overall accuracy of 78.24% and a Mean Absolute Error (MAE) of 0.3295. Specifically, the model achieved a 93.77% success rate in identifying human-written texts, though it logged a lower detection rate of 62.62% for academic AI text structures. The final model was successfully deployed as a web application using Streamlit.
Development of a Rental Vehicle Tax Monitoring System Using the RAD Method and Automated Reminder Notifications Yulia Hapsari; Wiranty; Desi Apriyanty; Alem Pameli
Jurnal Teknologi Informatika dan Komputer Vol. 12 No. 1 (2026): Jurnal Teknologi Informatika dan Komputer
Publisher : Universitas Mohammad Husni Thamrin

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37012/jtik.v12i1.3557

Abstract

Efficient management of vehicle tax obligations is essential for rental companies to ensure operational continuity and regulatory compliance. However, PT Go Rental still relies on manual procedures for recording tax information and reminding users of upcoming tax due dates. This practice increases the risk of delayed tax payments, difficulties in monitoring vehicle tax status, and potential administrative sanctions. To overcome the identified challenges, this research focuses on developing a web-based vehicle tax monitoring system designed to support a more streamlined and integrated tax management process. The system was built using the Rapid Application Development (RAD) approach, which promotes iterative system development and continuous user participation throughout the stages of requirements analysis, system design, implementation, and evaluation. The application was developed using PHP with the Laravel framework, while MySQL was employed for data storage and management. The functionality of the proposed system was assessed through black-box testing to ensure that each module operated in accordance with the defined specifications. The resulting application offers various features, including vehicle tax tracking, vehicle information management, tax payment reporting, and automated reminder notifications for upcoming tax due dates. The testing process confirmed that all system components functioned as intended and effectively assisted users in managing vehicle tax information. Furthermore, the implementation of the system is expected to minimize delayed tax payments, enhance administrative performance, and improve the availability of monitoring information for organizational decision-making.
Design of a Local Game News Website Using the Waterfall Method and the Laravel Framework Royan Habibie Sukarna; Fatwaraga Rafsanjani; Fitri Damyati; Haries Anom Susetyo Aji Nugroho
Jurnal Teknologi Informatika dan Komputer Vol. 12 No. 1 (2026): Jurnal Teknologi Informatika dan Komputer
Publisher : Universitas Mohammad Husni Thamrin

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37012/jtik.v12i1.3559

Abstract

The video game industry in Indonesia is showing an upward trend, marked by the emergence of various local game developers and growing public interest. However, the availability of information platforms specifically dedicated to local games remains limited and scattered across various sources. This situation makes it difficult to access structured and centralized information. This study aims to design and develop a web-based news platform focusing on local Indonesian video games using the Waterfall methodology and Laravel framework. The development process follows a structured approach including requirement analysis, system design, implementation, and testing. Data were collected through user preference questionnaires, observation of similar websites, and post-development evaluation. The system was developed with core features such as responsive interface, search functionality, and SEO optimization. Black-box testing results show that all system functionalities operate correctly. User evaluation involving 25 indicators resulted in a satisfaction score of 90.8% with an average rating of 4.54 out of 5. These results indicate that the system is effective, user-friendly, and meets user needs. The study concludes that the Waterfall method is suitable for structured web development and Laravel supports efficient system implementation.
Sentiment Analysis of the Indonesian Megathrust Earthquake and Tsunami Issue Using BERT and Roberta Methods Hendra Rahman; Taswanda Taryo; Sudarno Wiharjo
Jurnal Teknologi Informatika dan Komputer Vol. 12 No. 1 (2026): Jurnal Teknologi Informatika dan Komputer
Publisher : Universitas Mohammad Husni Thamrin

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37012/jtik.v12i1.3563

Abstract

The megathrust earthquake in Indonesia is a major potential natural disaster capable of triggering high-magnitude earthquakes and tsunamis, thereby influencing public perception. This study aims to analyze public opinion and identify the main topics related to the megathrust earthquake issue using Bidirectional Encoder Representations from Transformers (BERT) and Robustly Optimized BERT Pretraining Approach (RoBERTa) models. The dataset consists of 16,592 comments collected from the X social media platform during the period 2012–2025, which were classified into three sentiment categories, positive, negative, and neutral. The research methodology included exploratory data analysis, text preprocessing, model training, and evaluation using four experimental scenarios. The results indicate that the best performance was achieved using an 80:10:10 train–validation test split with ten training epochs. The BERT model outperformed RoBERTa, achieving an accuracy of 92,4350%, precision of 92,4291%, recall of 92,4350%, and F1-score of 92,4292%. These findings demonstrate that BERT is more effective in capturing the linguistic context of the Indonesian language. Furthermore, this study contributes to the advancement of artificial intelligence-based sentiment analysis for monitoring public opinion on disaster-related issues and provides a valuable foundation for developing more effective risk communication strategies, disaster mitigation education, and evidence-based policymaking that is more responsive to public perception.
Pengembangan Aplikasi Manajemen Inventaris Berbasis Android Guna Digitalisasi UMKM di Wilayah Jakarta Abu Sopian; Yohanes Bowo Widodo; Mohammad Ikhsan Saputro; Sondang Sibuea; Muhammad Ridwan Effendi
Jurnal Teknologi Informatika dan Komputer Vol. 10 No. 2 (2024): Jurnal Teknologi Informatika dan Komputer
Publisher : Universitas Mohammad Husni Thamrin

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37012/jtik.v10i2.3581

Abstract

Manajemen inventaris konvensional pada Usaha Mikro, Kecil, dan Menengah (UMKM) retail di wilayah DKI Jakarta masih menghadapi kendala struktural yang masif, seperti tingginya risiko salah hitung stok, selisih data logistik, serta keterlambatan kronis dalam proses pengadaan kembali barang dagangan yang habis akibat ketiadaan sistem pencatatan yang terintegrasi. Penelitian ini bertujuan untuk melakukan rekayasa perangkat lunak guna membangun aplikasi manajemen inventaris mandiri berbasis Android yang andal dan adaptif untuk mengakselerasi digitalisasi operasional internal pelaku UMKM retail. Perangkat lunak ini dikembangkan secara terstruktur menggunakan metodologi pengembangan sistem model Waterfall dengan mengimplementasikan bahasa pemrograman Kotlin pada lapisan antarmuka pengguna, serta memanfaatkan mesin basis data SQLite lokal tertanam sebagai media penyimpanan data utama guna mewujudkan arsitektur offline-first. Hasil penelitian menunjukkan bahwa aplikasi yang dibangun berhasil mengintegrasikan seluruh fungsionalitas utama yang mencakup komponen 'Menu Barang Masuk' untuk melakukan update penambahan jumlah stok secara otomatis (+) dan 'Menu Barang Keluar' untuk melakukan update pengurangan jumlah stok secara real-time (-). Di sisi penyimpanan persisten, basis data SQLite lokal secara otonom mengolah sirkulasi arus logistik tersebut dan sukses memicu fungsi 'Peringatan Stok Kritis (<)' ketika kuantitas barang berada di bawah batas aman, sekaligus menginisiasi fitur 'Generate Laporan Otomatis' dalam format digital. Validasi keabsahan sistem melalui metode Black-box testing memberikan hasil pemenuhan fitur sebesar 100% valid tanpa adanya galat kritis, serta terbukti secara signifikan memangkas waktu stock opname harian para staf dari yang semula memakan waktu hitungan jam menjadi kurang dari 30 menit saja. Rekomendasi operasional dari penelitian ini ditujukan bagi pengembang sistem selanjutnya untuk menambahkan modul sinkronisasi data awan hibrida (hybrid cloud-sync) guna memfasilitasi kebutuhan pemantauan data jarak jauh secara multi-user oleh pemilik usaha tanpa mengorbankan ketahanan fitur luring aplikasi.
Analysis of Student Sentiments towards the Use of Artificial Intelligence in Education Using the Naïve Bayes Algorithm Stevannie Evanjelica Lourensia; Ni Made Satvika Iswari; Eddy Muntina Dharma
Jurnal Teknologi Informatika dan Komputer Vol. 12 No. 1 (2026): Jurnal Teknologi Informatika dan Komputer
Publisher : Universitas Mohammad Husni Thamrin

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37012/jtik.v12i1.3606

Abstract

The rapid development of Artificial Intelligence (AI) has significantly transformed the field of education. Generative AI applications such as ChatGPT, Gemini, and Microsoft Copilot are increasingly utilized by students to obtain information, assist in completing academic tasks, and improve learning effectiveness. This study aims to analyze students' sentiment toward the use of AI in education using the Naïve Bayes algorithm. Data were collected through questionnaires distributed to high school students in Palu City, Central Sulawesi Province, and processed through text preprocessing stages, including case folding, tokenization, stopword removal, and stemming. The dataset was divided into training and testing data to evaluate the model using a confusion matrix, accuracy, precision, recall, and F1-score. The findings indicate that the Naïve Bayes algorithm performs well in classifying students' sentiments toward AI in education. Most students expressed neutral sentiments regarding the use of AI in learning, while others reported positive and negative perceptions related to its benefits and potential risks. This study provides empirical insights into students' perceptions of AI in education and may serve as a reference for technology-based learning strategies. Students are encouraged to use AI wisely while maintaining critical thinking, analytical skills, and independent learning.

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