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All Journal Jiko (Jurnal Informatika dan komputer) ProBisnis : Jurnal Manajemen Jurnal Pengabdian Kepada Masyarakat Indonesia
Arif Fitra Setyawan
Universitas Widya Husada Semarang
Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Economics, Econometrics & Finance Education Engineering Industrial & Manufacturing Engineering Public Health Social Sciences

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Design and Development of an Online Queue Administration System in Health Services (Study Case: Klinik Halyna Pageruyung Kendal) Arif Fitra Setyawan; Amelia Devi Putri Ariyanto; Ari Dina Permana Citra
ProBisnis : Jurnal Manajemen Vol. 14 No. 3 (2023): June: Management Science
Publisher : Lembaga Riset, Publikasi dan Konsultasi JONHARIONO

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Queuing culture reflects the identity of an advanced nation, because it forms a social attitude that is disciplined and respects other people. Apart from respecting each other, queuing in an orderly manner and not overtaking or breaking through the queue includes respecting the rights of others. Online queuing systems offer many benefits, including elimination of queues, increased efficiency in service delivery, and increased customer satisfaction. They can be applied to a wide range of industries, including healthcare, banking, retail and government services. In a health service agency such as a clinic, service for customer satisfaction is very important, so efforts are always made to improve service quality. An online queuing system is a digital system that allows users to enter a virtual queue, where they wait their turn to access services or receive assistance. The system provides users with a way to book time slots, secure their place in the queue, and receive real-time updates on the status of their positions. To implement an online queuing system requires investment in appropriate hardware and software, such as a cloud-based queue management system or mobile application. Overall, online queuing systems can help optimize service delivery, reduce waiting times, and improve the overall customer experience. The existence of a queuing system makes it easy for people to manage time efficiently. The increasing need for fast services requires that every public service, especially health clinics, have a queuing system. With the Online Queue Administration System to help queue administration problems.
Sosialisasi Sistem Informasi Penelitian dan Pengabdian LPPM UWHS Rozaq Isnaini Nugraha; Arif Fitra Setyawan
Jurnal Pengabdian kepada Masyarakat Indonesia (JPKMI) Vol. 5 No. 2 (2025): Agustus: Jurnal Pengabdian Kepada Masyarakat Indonesia (JPKMI)
Publisher : Pusat Riset dan Inovasi Nasional

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55606/jpkmi.v5i2.7899

Abstract

The management of research and community service data in higher education institutions is crucial for achieving Key Performance Indicators (IKU) and supporting the Merdeka Belajar Kampus Merdeka (MBKM) policy. At Widya Husada University Semarang (UWHS), data recording and reporting processes for research and community service still face various challenges, including low efficiency, lack of accuracy, and limited system integration. To address these issues, a community service program was conducted through the socialization and implementation of a web-based Internal Research and Community Service Information System (BIMA) under the UWHS Research and Community Service Institute (LPPM). The method employed consists of five stages: socialization, training, technology implementation, mentoring and evaluation, and program sustainability. The results indicate that the developed system successfully enhances reporting efficiency, process transparency, and digital literacy among lecturers. Moreover, it improves lecturers’ ability to independently manage their research and community service proposals, especially in preparing for national research and service grant opportunities.
CLASSIFICATION OF DENGUE FEVER DISEASE USING A MACHINE LEARNING-BASED RANDOM FOREST ALGORITHM ARIF FITRA SETYAWAN; Amelia Devi Putri Ariyanto; Fari Katul Fikriah
JIKO (Jurnal Informatika dan Komputer) Vol 7 No 2 (2024)
Publisher : Program Studi Teknik Informatika Universitas Khairun

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33387/jiko.v7i2.8496

Abstract

Dengue Hemorrhagic Fever (DHF) is a tropical disease that often results in high morbidity and mortality rates. Early diagnosis of DHF is crucial to mitigate its adverse effects. However, manual diagnostic processes are often inefficient and prone to errors. This study aims to develop a DHF classification model using the Random Forest algorithm, which is expected to assist in the early diagnosis of this disease. The methodology used in this research is CRISP-DM (Cross-Industry Standard Process for Data Mining), which includes the stages of Business Understanding, Data Understanding, Data Preparation, Modeling, Evaluation, and Deployment. Data was obtained from kaggle.com, and during the Data Preparation stage, missing values were removed, categorical features were encoded, data was normalized, and split into training and testing sets. The research results show that the Random Forest model has an accuracy of 88.5%, precision of 88.2%, recall of 65.2%, F1-score of 74.9%, and ROC AUC of 0.810. Feature importance analysis revealed that the Gender_Male and Body_Pain features have the largest contributions in DHF classification. Although the model demonstrated high accuracy and precision, the lower recall value indicates that some positive cases were missed, requiring further improvements. The Random Forest can be used as a tool for early DHF diagnosis, but further adjustments are necessary to enhance its performance. This research provides insights into the contributing factors for DHF diagnosis and the practical application potential of this model in medical decision support systems.
OPTIMIZING GPT AND INDOBERT FOR SENTIMENT ANALYSIS AND CONSUMER TREND PREDICTION ON LAZADA PRODUCT REVIEWS Arif Fitra Setyawan; Rozaq Isnaini Nugraha
JIKO (Jurnal Informatika dan Komputer) Vol 8 No 2 (2025)
Publisher : Program Studi Teknik Informatika Universitas Khairun

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33387/jiko.v8i2.10066

Abstract

Sentiment analysis has become a vital approach in understanding customer opinions through textual reviews. One of the primary challenges in sentiment classification lies in class imbalance, where positive reviews often dominate the dataset. This imbalance causes machine learning models to be biased toward the majority class and underperform in detecting minority sentiments. To address this issue, this study applies the Synthetic Minority Oversampling Technique (SMOTE) and evaluates the performance of two Transformer-based models: Generative Pre-trained Transformer (GPT) as a baseline and IndoBERT as the primary model. The dataset consists of 12,704 product reviews from Lazada, obtained from the Kaggle platform, and is categorized into three sentiment classes (positive, neutral, negative). The data was split into 80% for training and 20% for testing. After preprocessing and applying SMOTE for data balancing, the fine-tuned IndoBERT model achieved the best performance with an accuracy of 88%, significantly outperforming GPT, which yielded only 47% accuracy in a zero-shot setting. These findings highlight the critical role of addressing data imbalance and selecting context-aware models for improving sentiment classification accuracy in Indonesian language texts
Co-Authors Amelia Devi Putri Ariyanto Ari Dina Permana Citra Fari Katul Fikriah Rozaq Isnaini Nugraha Rozaq Isnaini Nugraha