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All Journal Syntax Jurnal Informatika Scan : Jurnal Teknologi Informasi dan Komunikasi Proceeding International Conference on Information Technology and Business Jurnal Informatika dan Teknik Elektro Terapan Journal of Information System JOIV : International Journal on Informatics Visualization INTEGER: Journal of Information Technology Jurnal Penelitian Pendidikan IPA (JPPIPA) JPP IPTEK (Jurnal Pengabdian dan Penerapan IPTEK) bit-Tech JATI (Jurnal Mahasiswa Teknik Informatika) CICES (Cyberpreneurship Innovative and Creative Exact and Social Science) Jifosi Jurnal Pengabdian kepada Masyarakat Nusantara Nusantara Science and Technology Proceedings Jurnal Teknik Informatika (JUTIF) Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer) SINTA Journal (Science, Technology, and Agricultural) East Asian Journal of Multidisciplinary Research (EAJMR) Jurnal Teknologi dan Manajemen Industri Terapan Jurnal Teknik Informatika dan Teknologi Informasi J-Icon : Jurnal Komputer dan Informatika TAMIKA: Jurnal Tugas Akhir Manajemen Informatika & Komputerisasi Akuntansi Inspiration: Jurnal Teknologi Informasi dan Komunikasi Jurnal Sistem Informasi dan Ilmu Komputer Jurnal Elektronika dan Teknik Informatika Terapan Jurnal Informatika Polinema (JIP) VISA: Journal of Vision and Ideas Jupiter: Publikasi Ilmu Keteknikan Industri, Teknik Elektro dan Informatika Merkurius: Jurnal Riset Sistem Informasi dan Teknik Informatika Jurnal Teknik Informatika dan Teknologi Informasi Jurnal Sistem Informasi dan Ilmu Komputer
Firza Prima Aditiawan
Department Of Informatics, Universitas Pembangunan Nasional “Veteran” Jawa Timur, Surabaya 60294, Indonesia
Aerospace Engineering Agriculture, Biological Sciences & Forestry Astronomy Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Medicine & Pharmacology Physics Public Health Social Sciences Veterinary Other

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A Web-Based Online Reservation System with Personalized Tourism Recommendations Using Content-Based Filtering Amelia, Rizky; Nurlaili, Afina Lina; Aditiawan, Firza Prima
bit-Tech Vol. 8 No. 3 (2026): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i3.3382

Abstract

The rapid growth of digital technologies has transformed the tourism industry and increased the need for personalized recommendation systems to enhance user experience and business competitiveness. However, many small- and medium-scale travel agencies still rely on manual reservation processes and social media–based promotions, which limit service efficiency and personalization. This study designs and implements a web-based reservation and tourism recommendation system for Sumovacation Tour using a Content-Based Filtering approach enhanced with feature weighting and cosine similarity. The main novelty of this study lies in the feature weighting mechanism, which assigns different importance levels to package attributes such as activities, travel duration, package type, and budget, improving recommendation relevance compared to standard content-based methods. Data were collected from Google Maps reviews in 2025, resulting in approximately 300 rating and review entries. The recommendation engine computes weighted relevance scores from user preference tags and package metadata to generate personalized recommendations. System functionality was validated using Black Box Testing, where all core workflows successfully passed, while usability evaluation using the USE Questionnaire showed high user acceptance, with usefulness, satisfaction, and ease of use each scoring 94.4%, and ease of learning reaching 95.2%. During testing, challenges related to data consistency and user input variation were addressed through input validation. The results show that the proposed system improves recommendation relevance while enhancing operational efficiency by reducing manual booking handling and supporting digital reservation management.
Aplikasi OMR untuk Pemeriksaan Lembar Jawaban menggunakan DexiNed Prastyo, Kus Dwi; Junaidi, Achmad; Aditiawan, Firza Prima
bit-Tech Vol. 8 No. 3 (2026): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i3.3425

Abstract

Digital image processing is a field of computer science that focuses on analyzing and interpreting digital images to extract meaningful information. One of its applications is Optical Mark Recognition (OMR), a technology used to detect marks on documents. OMR is commonly utilized for evaluating answer sheets. However, conventional OMR systems typically rely on specialized scanners that are expensive and lack flexibility. Although Computer-Based Testing (CBT) offers the convenience of automated scoring, its implementation heavily depends on the availability of technological infrastructure such as computers, internet connectivity, and a stable power supply. This study develops a real-time Optical Mark Recognition (OMR) application capable of performing answer sheet assessment directly on the client side. The system utilizes the DexiNed method for edge detection of the answer areas. The application is web-based and built using JavaScript and OpenCV.js to process images directly from the user's device camera. Testing was carried out under various scenarios, including different lighting intensities, scanner positions, pencil types, and shading quality. The results show that the application can detect marked answers with an accuracy up to 100%, although some limitations were observed under certain technical conditions. Weaknesses were found in low lighting conditions using a 5 watt lamp at a distance of 3 meters, light reflections, and the camera angle was not aligned with the answer sheet. Overall, the application provides an efficient and flexible alternative for answer sheet assessment without requiring dedicated scanning devices, making it suitable for educational institutions with limited infrastructure.
Klasifikasi Rating Film Berdasarkan Genre Menggunakan XGBoost dan LightGBM serta Analisis SHAP Roiqoh, Aprinia Salsabila; Parlika, Rizky; Aditiawan, Firza Prima
JURNAL FASILKOM Vol. 16 No. 1 (2026): Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer)
Publisher : Unversitas Muhammadiyah Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37859/jf.v16i1.11273

Abstract

Movie rating is often used as an indicator of film quality and audience satisfaction. With the large availability of movie data on online platforms, machine learning techniques can be used to analyze the relationship between film characteristics and rating patterns. One important attribute that can influence movie ratings is genre. This study aims to classify movie ratings based on genre using the XGBoost and LightGBM algorithms and to analyze the contribution of each genre using SHAP (SHapley Additive Explanations). Movie data were collected from The Movie Database (TMDB) API and processed through several preprocessing stages including genre separation, data cleaning, one-hot encoding, and rating categorization. The dataset was then divided into training and testing data with a ratio of 70:30. The classification results show that XGBoost achieved an accuracy of 0.53, slightly higher than LightGBM with an accuracy of 0.52. Further analysis using SHAP indicates that genres such as Horror, Drama, Action, and Comedy have the highest global importance in the classification model. Meanwhile, the analysis of high-rating class predictions shows that Drama has the largest contribution to predicting movies with high ratings. The findings indicate that movie genres have a measurable influence on rating classification, although the importance of genres in the machine learning model does not always align with their average rating values.
Prediksi Gangguan Kesehatan Mental pada Kalangan Mahasiswa Menggunakan Metode Pseudo-Labeling dan Algoritma Regresi Logistik Anggraini Puspita Sari; Dwi Arman Prasetya; Firza Prima Aditiawan; Muhammad Muharrom Al Haromainy
TAMIKA: Jurnal Tugas Akhir Manajemen Informatika & Komputerisasi Akuntansi Vol 4 No 2(SEMNASTIK) (2024): TAMIKA: Jurnal Tugas Akhir Manajemen Informatika & Komputerisasi Akunt
Publisher : Universitas Methodist Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46880/tamika.Vol4No2(SEMNASTIK).pp40-48

Abstract

Mental illness is a health condition that alters a person's thoughts, feelings, or behaviors, leading to distress and difficulty in maintaining a normal life. Mental health issues should not be taken lightly due to the challenges associated with diagnosis. Many students tend to experience mental health problems at various stages of their education, from diploma programs to doctoral studies. This situation becomes more critical as students approach the end of their studies and anticipate future prospects. This article explores the mental health status of students through symptoms, using logistic regression methods for prediction based on the dataset used. In this study, two types of data are employed: labeled dataset and unlabeled dataset, which are combined to create a semi-supervised learning approach. Labeled dataset is classified using a logistic regression algorithm, while unlabeled dataset employs the pseudo-labeling method. The analysis and modeling of the dataset indicate that the comparison between labeled and unlabeled dataset can significantly affect accuracy and processing time. Furthermore, the use of the pseudo-labeling method with the logistic regression algorithm is well-suited for the mental health case study, achieving an accuracy of 98% with a labeled to unlabeled dataset ratio of 1:2.
Sistem Pendukung Keputusan Perdagangan Cryptocurrency Menggunakan Pembobotan Kombinasi Indikator EMA, RSI, MACD, dan Bollinger Bands M. Zaky Pria Maulana; Rizky Parlika; Firza Prima Aditiawan
JOINS (Journal of Information System) Vol 11 No 1 (2026): (Desember 2025 - Mei 2026)
Publisher : Fakultas Ilmu Komputer, Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33633/joins.v11i1.15776

Abstract

Cryptocurrency trading has rapid and significant price changes that cause investors to make decisions based solely on intuition when buying assets, potentially leading to a risk of loss. Therefore, this research aims to develop a cryptocurrency trading decision support system (DSS) using a combination of technical indicators, namely EMA, RSI, MACD, and Bollinger Bands. The system is designed to assist users in making more objective trading decisions based on historical data. This study applies weighted indicator combinations ranging from 0 to 4, resulting in 625 weight combinations evaluated thru backtesting using ROI, Win Rate, and MDD metrics. Based on the test results, the weighted indicator combination outperformed single indicators by achieving an ROI increase of up to 2222.35% on the SOLUSDT asset. In addition, the approach improved signal accuracy, as shown by the increase in Win Rate on ETHUSDT from 35.21% to 47.28% and on SOLUSDT from 32.84% to 58.11%. Furthermore, the method was effective in mitigating risk, indicated by the reduction of MDD on ETHUSDT from 50.04% to 41.35%. The system was successfully implemented as a web-based application integrated with Telegram notifications to deliver analysis results to users.
Co-Authors Achmad Junaidi Adzanil Rachmadhi Putra Afina Lina Nurlaili Agil Sakinah, Fenti Agung Mustika Rizki Agung Mustika Rizki, Agung Mustika Akbar, Fawwaz Ali Akhmad Fauzi Al Fathoni, Hanif Alit, Ronggo Alwin, Muhammad Izdihar Andreas Nugroho Sihananto Anggraini Puspita Sari Anggriawan, Teddy Prima Aniisah Eka Rahmawati Ardilla, Aufa ASHARI, FAISAL Boy Diego Lumwartono Davila Erdianita Dimas Putra Andaru Dimas Putra Andaru Dwi Arman Prasetya Dwi Arman Prasetya Dwi Rahma Putri, Septiani Eka Prakarsa Mandyartha Eka Zuni Selviana EKO WAHYUDI Eriyansyah Yusuf Suwandana Erlangga Putra Ramadhan Eva Yulia Puspaningrum Fetty Tri Anggraeny Fidela Carissa Aramintha Firmansyah Firdaus Anhar Gusti Eka Yuliastuti Hamidah Hendrarini Hardianto, Eragradiansyah Hariyanti, Nanda Syarla Henni Endah Wahanani Herdi Rofaldi Hidra Amnur I Gede Susrama Idhom, Mohammad Iriansah, Ogy Rachmad Ismiati, Suci Khairil Amin, Mohammad Lina Nurlaili, Afina M. Zaky Pria Maulana Made Hanindia Prami Swari Mafaza, Rima Muttaqina Mahanani, Anajeng Esri Edhi Maulana, Hendra Mohammad Idhom Mubarokah Muhammad Eko Prasetyo Muhammad Izdihar Alwin Muhammad Izdihar Alwin Muhammad Muharrom Al Haromainy Mustika Rizki, Agung Muttaqin, Faisal Muttaqin, Faisal Nugroho Gultom, Wahyu Nugroho Sihananto, Andreas Nur Aini Ersanti Nurlaili, Afina Lina Parlika, Rizky Pradana Ariando, Aldo Prastyo, Kus Dwi Pratama Wirya Atmaja Pratama Wirya Atmaja Puspaningrum, Eva Y Rahmawati, Aniisah Eka Raviy Bayu Setiaji Retno Mumpuni Rizki, Agung Mustika Rizky Amelia Rizqulloh Zain, Muhammad Dhiya'ulhaq Roiqoh, Aprinia Salsabila Samdono, Arif Saputra, Wahyu Syaifullah Jauharis Shabika Aqmarina, Azzuraa Soedarto, Teguh Suprapto, Claudia Millennia Vita Via, Yisti Wardana, Nabila Sya’bani Wicaksa Putra Pribadi, Achareeya Widoretno, Astrini Aning Winata, Chycik Ayu Wirya Atmaja, Pratama Yunizar, Sri Fatmawati