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IT-Explore: Jurnal Penerapan Teknologi Informasi dan Komunikasi
Published by Universitas Kristen Satya Wacana
ISSN : 28287940     EISSN : 28291727     DOI : https://doi.org/10.24246/itexplore.v1i1.2022
Core Subject : Science, Engineering,
Computer Science & IT Control & Systems Engineering
IT-Explore: Jurnal Penerapan Teknologi Informasi dan Komunikasi merupakan jurnal ilmiah tentang penelitian penerapan Teknologi Informasi dalam berbagai bidang, terbit tiga kali dalam setahun, yaitu pada bulan Januari, Mei, dan September untuk masing-masing volumenya. IT-Explore menerima artikel ilmiah hasil-hasil penelitian di bidang penerapan Teknologi Informasi, yang harus didasarkan pada hasil penelitian yang mengetengahkan urgensi, manfaat dan tujuan penelitian. Ruang Lingkup artikel ilmiah yang dapat diterbitkan di IT-Explore: Jurnal Penerapan Teknologi Informasi dan Komunikasi mencakup, namun tidak terbatas pada Teknologi Informasi, Sistem Informasi, Bisnis Digital, Komunikasi Visual, Teknologi Komunikasi, Pendidikan Teknologi Informasi, Multimedia, Sains Informasi, dan Penerapan TI dalam berbagai bidang.
Arjuna Subject : Ilmu Komputer (semua kategori) - Aplikasi Ilmu Komputer
Articles 104 Documents
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Rancang bangun aplikasi absensi bimbingan skripsi berbasis web menggunakan framework laravel Muhamad Fikri Surya
IT Explore: Jurnal Penerapan Teknologi Informasi dan Komunikasi Vol 5 No 2 (2026): IT-Explore Juni 2026
Publisher : Fakultas Teknologi Informasi, Universitas Kristen Satya Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24246/itexplore.v5i2.2026.pp187-199

Abstract

The thesis supervision process in higher education institutions is still frequently conducted manually, which may lead to inefficiencies in recording supervision data. This study focuses on the development and implementation of a web-based thesis supervision attendance application designed to facilitate attendance documentation, supervision session administration, and systematic monitoring of thesis supervision history. The methodology applied in this study is the Waterfall model, which includes the phases of requirements analysis, system design, implementation, and testing. The application was developed using the Laravel framework and a MySQL database. The system design was modeled using Unified Modeling Language (UML), while the validation process was conducted through Black Box Testing techniques. The research findings indicate that the developed application is capable of performing real-time supervision attendance recording, managing supervision information, and generating attendance reports effectively and efficiently. It can be concluded that the web-based thesis supervision attendance application improves the efficiency and accuracy of supervision record management and supports a more effective thesis supervision monitoring process..
Analisis tingkat pemahaman mahasiswa teknologi rekayasa informatika industri terhadap penggunaan bahasa pemrograman python dalam pembelajaran Pemrograman Ali Musthofa Baharudin; Aqsha Maulana Ilham; Arum Sita Resmi; Bella Firdha Azkia; Naufal Reswara; Ihsan Tanama Sitio
IT Explore: Jurnal Penerapan Teknologi Informasi dan Komunikasi Vol 5 No 2 (2026): IT-Explore Juni 2026
Publisher : Fakultas Teknologi Informasi, Universitas Kristen Satya Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24246/itexplore.v5i2.2026.pp151-166

Abstract

Python programming has become a fundamental competence in the digital era, yet students often struggle to transform algorithmic logic into functional code. This gap between conceptual understanding and practical implementation skills requires a thorough investigation into learning challenges within the Industrial Informatics Engineering Technology (TRIN) program at Politeknik Manufaktur Bandung. Grounded in Bloom's Revised Taxonomy and Cognitive Load Theory, this descriptive quantitative study utilized a Likert-scale questionnaire and an objective comprehension test administered to 87 third-year students. Data were analyzed using descriptive statistics to map performance across three aspects: conceptual understanding, syntactic comprehension, and implementation ability. Results indicate the conceptual aspect achieved the highest average of 4.15, followed by syntax at 3.56 and implementation at 3.54, with objective test accuracy rates of 76.09%, 65.52%, and 67.36%, respectively. Major obstacles identified include difficulties with looping, debugging, and comparison operators. Therefore, enhanced structured practice and Project-Based Learning approaches are recommended to strengthen students' implementation competencies.
Optimasi bayesian pada model long short-term memory untuk prediksi harga saham perbankan Indonesia Wresti Andriani; Gunawan; Naella Nabila Putri Wahyuning Naja
IT Explore: Jurnal Penerapan Teknologi Informasi dan Komunikasi Vol 5 No 2 (2026): IT-Explore Juni 2026
Publisher : Fakultas Teknologi Informasi, Universitas Kristen Satya Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24246/itexplore.v5i2.2026.pp217-231

Abstract

Bank stock price prediction is an important topic in the application of information technology because stock price movements are dynamic, sequential, and influenced by historical market patterns. This study aims to predict Indonesian banking stock prices using the Long Short-Term Memory method and evaluate the effect of Bayesian Optimization on model performance. The data used in this study consists of daily historical stock data of BBCA, BBNI, BBRI, BBTN, and BMRI from May 4, 2020, to May 4, 2026, obtained from Yahoo Finance. The input features include opening price, highest price, lowest price, closing price, and trading volume, while the prediction target is the stock closing price. The results show that the baseline model produced MAPE values ranging from 1.892% to 3.147%. The best baseline performance was obtained on BBCA with an R² value of 0.933, followed by BBTN with an R² value of 0.902. After optimization, performance improvement occurred on BBTN, with MAPE decreasing from 3.147% to 2.482% and R² increasing from 0.902 to 0.935. For BMRI, MAPE decreased from 2.385% to 2.206%, and R² increased from 0.687 to 0.743. This study concludes that Long Short-Term Memory can be used to predict Indonesian banking stock prices, while Bayesian Optimization can selectively improve model performance depending on the characteristics of each stock dataset.
Analisis sentimen pengguna tiktok terhadap program makan bergizi gratis menggunakan metode naive bayes Iza Rifna; Nurdin Nurdin
IT Explore: Jurnal Penerapan Teknologi Informasi dan Komunikasi Vol 5 No 2 (2026): IT-Explore Juni 2026
Publisher : Fakultas Teknologi Informasi, Universitas Kristen Satya Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24246/itexplore.v5i2.2026.pp241-251

Abstract

The Free Nutritional Meal Program (MBG) is a government policy that is widely discussed by the public through social media, especially TikTok. Various comments that have emerged indicate differences in public opinion towards the program, so an analysis is needed to determine the tendency of public sentiment. This study aims to analyze TikTok user sentiment towards the Free Nutritional Meal Program using the Naive Bayes method. The research method is carried out through several steps, namely collecting TikTok comment data, preprocessing text, labeling sentiment data into positive, negative, and neutral, feature transformation using TF-IDF, and classification using the Naive Bayes algorithm. Based on the analysis of 500 comment data, the results show that positive sentiment dominates public opinion by 42% (210 data), followed by negative sentiment by 36% (180 data), and neutral sentiment by 22% (110 data). Testing the classification model using Naive Bayes produces excellent performance with an accuracy rate of 86%, precision of 84%, recall of 85%, and F1-score of 84%. The conclusion of this study shows that the Naive Bayes method is effective as an approach in social media sentiment analysis to map public responses to government policies.

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