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All Journal Jurnal Teknik Informatika (JUTIF) JSIKTI (Jurnal Sistem Informasi dan Komputer Terapan Indonesia)

Prakoso, Paholo Iman

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Author-ID : 2057213

Computer Science & IT

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Forecasting Kunjungan Wisatawan Dengan Long Short Term Memory (LSTM) Sugiartawan, Putu; Jiwa Permana, Agus Aan; Prakoso, Paholo Iman
Jurnal Sistem Informasi dan Komputer Terapan Indonesia (JSIKTI) Vol 1 No 1 (2018): September
Publisher : INFOTEKS (Information Technology, Computer and Sciences)

Bali is one of the favorite tourist attractions in Indonesia, where the number of foreign tourists visiting Bali is around 4 million over 2015 (Dispar Bali). The number of tourists visiting is spread in various regions and tourist attractions that are located in Bali. Although tourist visits to Bali can be said to be large, the visit was not evenly distributed, there were significant fluctuations in tourist visits. Forecasting or forecasting techniques can find out the pattern of tourist visits. Forecasting technique aims to predict the previous data pattern so that the next data pattern can be known. In this study using the technique of recurrent neural network in predicting the level of tourist visits. One of the techniques for a recurrent neural network (RNN) used in this study is Long Short-Term Memory (LSTM). This model is better than a simple RNN model. In this study predicting the level of tourist visits using the LSTM algorithm, the data used is data on tourist visits to one of the attractions in Bali. The results obtained using the LSTM model amounted to 15,962. The measured value is an error value, with the MAPE technique. The LSTM architecture used consists of 16 units of neuron units in the hidden layer, a learning rate of 0.01, windows size of 3, and the number of hidden layers is 1.

Sistem Keamanan Data Text Dengan Data Encryption Standard (DES) dan Metode Least Significant Bit (LSB) Pratistha, Indra; Prakoso, Paholo Iman
Jurnal Sistem Informasi dan Komputer Terapan Indonesia (JSIKTI) Vol 1 No 2 (2018): December
Publisher : INFOTEKS (Information Technology, Computer and Sciences)

Abstract Current information can be delivered quickly without knowing the geographical boundaries using the internet. However, the information transmitted can be intercepted by the middle of the road which is not desired, then the security of the data actually becomes a very important issue. The development of computer systems and networks and the interconnection through the Internet has increased, of course, require data security and reliable message in order to avoid the attack. To secure internet data or message network necessary cryptographic encryption methods, one method of data encryption that there is a method of data encryption standard (DES) and subsequently inserted into the Lakeside digital images (steganography) using the method of least significant bit (LSB).

Sistem Pendukung Keputusan Kelompok Promosi Jabatan dengan Metode AHP dan BORDA Sugiartawan, Putu; Prakoso, Paholo Iman
Jurnal Sistem Informasi dan Komputer Terapan Indonesia (JSIKTI) Vol 1 No 4 (2019): June
Publisher : INFOTEKS (Information Technology, Computer and Sciences)

Abstract Determination of the best employees in a company aims to improve competitiveness and give awards to employees who have high dedication to the company. Difficulties in evaluating employees are the many assessment factors and characteristics of the appraisal and who will provide the appraisal, so that the appraisal is appropriate and transparent. Employee performance appraisal is carried out by 2 assessors namely the company manager and the head of the human resource management department (HRD) and consists of a number of criteria. The assessment will be difficult if done manually considering that each assessor has their own preferences in conducting the assessment. To overcome this we need a computer system that helps decision making, namely a group decision support system (GDSS) determining the best employees in a company The group decision support system developed in this study uses the AHP and BORDA methods to assist group decision making. AHP method is used for decision making in each appraiser, while the Borda method is used to combine the decision results of each appraiser so that the final result is the best employee in the company. Based on the final results of the best employee determination system in the form of ranking the final value of each employee. The highest value is used as a recommendation as the best employee.

Penentuan Desa Wisata Terbaik di Kabupaten Tabanan dengan Model AHP dan BORDA Sugiartawan, Putu; Prakoso, Paholo Iman; Aryawan, I Made Gitra
Jurnal Sistem Informasi dan Komputer Terapan Indonesia (JSIKTI) Vol 2 No 1 (2019): September
Publisher : INFOTEKS (Information Technology, Computer and Sciences)

Tabanan Regency, is promoting the development of tourist villages and the development of many tourist villages in the area. The development of tourism villages can generate large regional income or income derived from the tourism sector. The problem is that not all tourist villages develop and generate revenue for the government, even many tourist villages are quiet from tourist visits. The selection of the best tourism village aims to find out what supporting factors can increase tourist visits and provide assistance to the tourist village. The purpose of the aid providers is to improve the quality of tourism in the area. The model used in the selection of tourist villages is the AHP and BORDA models. Both models are able to rank tourist villages from several criteria owned by tourist villages. The decision holder in this system is the Tourism Office of Tabanan Regency and the Province of Bali.

Predicting Anxiety of STMIK Palangkaraya Students Using K-Means Clustering and Gaussian Naïve Bayes Widyaningsih, Maura; Rosmiati, Rosmiati; Prakoso, Paholo Iman
Jurnal Teknik Informatika (Jutif) Vol. 7 No. 1 (2026): JUTIF Volume 7, Number 1, February 2026
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2026.7.1.5259

Academic anxiety is a common psychological problem experienced by students, especially before final exams, which impacts learning performance and mental well-being. This study aims to identify and predict students' anxiety levels using a Machine Learning approach, specifically the web framework Gradio, through a combination of the K-Means Clustering and Gaussian Naïve Bayes (GNB) methods. The research instrument used a Google Form-based questionnaire modified from the Zung Self-Rating Anxiety Scale (ZSAS) with 20 items (K1–K20) on a Likert scale (0–3). Data were obtained from 110 students of the Information Systems and Informatics Engineering Study Program at STMIK Palangkaraya. The research process consisted of five main stages: pre-processing, clustering using the K-Means algorithm, training the GNB classification model, evaluation, and prediction of new data. The clustering results categorized the data into three levels of anxiety: Low, Median, and High. The GNB model showed 95% accuracy with a balanced distribution of evaluation metrics (precision, recall, and F1 score). Comparison with other algorithms shows that while SVM achieved the highest accuracy (100%), GNB was more balanced in handling uneven class distributions and more practical for implementation in web-based systems. This prediction system has the potential to be used as an early detection tool for student anxiety, while also supporting educational institutions in designing more targeted psychological interventions. Further improvements can be made by expanding the scope of respondents, balancing the data distribution, and testing other machine learning methods to improve model generalization. The program and data are available at: https://github.com/maurawidya75/StudentAnxiety2025.

Co-Authors Agus Aan Jiwa Permana Aryawan, I Made Gitra Indra Pratistha Maura Widyaningsih, Maura PUTU SUGIARTAWAN Rosmiati Rosmiati

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