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ILKOM Jurnal Ilmiah
Published by Universitas Muslim Indonesia
ISSN : 20871716     EISSN : 25487779     DOI : -
Core Subject : Science,
Computer Science & IT
ILKOM Jurnal Ilmiah is an Indonesian scientific journal published by the Department of Information Technology, Faculty of Computer Science, Universitas Muslim Indonesia. ILKOM Jurnal Ilmiah covers all aspects of the latest outstanding research and developments in the field of Computer science, including Artificial intelligence, Computer architecture and engineering, Computer performance analysis, Computer graphics and visualization, Computer security and cryptography, Computational science, Computer networks, Concurrent, parallel and distributed systems, Databases, Human-computer interaction, Embedded system, and Software engineering.
Arjuna Subject : -
Articles 580 Documents
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Short-Term Load Forecasting using Artificial Neural Network in Indonesia Sylvia Jane Annatje Sumarauw
ILKOM Jurnal Ilmiah Vol 15, No 1 (2023)
Publisher : Prodi Teknik Informatika FIK Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v15i1.1512.72-81

Abstract

Short-term Load Forecast (STLF) is a load forecasting that is very important to study because it determines the operating pattern of the electrical system. Forecasting errors, both positive and negative, result in considerable losses because operating costs increase and ultimately lead to waste. STLF research in Indonesia, especially the State Electricity Company (PLN Sulselrabar), has yet to be widely used. Methods mainly used are manual and conventional methods because they are considered adequate. In addition, Indonesia's geographical conditions are extensive and diverse, and the electricity system is complex. As a result, the factors affecting each country's electricity demand are different, so unique forecasting methods are needed. Artificial Neural Network (ANN) is one of the Artificial Intelligent (AI) methods widely used for STLF because it can model complex and non-linear relationships from networks. This paper aims to build an STLF forecasting model that is suitable for Indonesia's geographical conditions using several ANN models tested. Based on several ANN forecasting models, the test results obtained the best model is Model-6 with ANN architecture (9-20-1). This model has one hidden layer, 20 neurons in the hidden layer, a sigmoid logistic activation function (binary sigmoid), and a linear function. Forecasting performance values obtained mean squared error (MSE), mean absolute error (MAE), and mean absolute percentage error (MAPE) of 430.48 MW2, 15.07 MW, and 2.81%, respectively.
Determining Eligible Villages for Mobile Services using K-NN Algorithm Anton Yudhana; Imam Riadi; M Rosyidi Djou
ILKOM Jurnal Ilmiah Vol 15, No 1 (2023)
Publisher : Prodi Teknik Informatika FIK Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v15i1.1546.11-20

Abstract

To maximize and get population document services closer to the community, the Disdukcapil district of Alor provides mobile services by visiting people in remote villages which difficult-to-reach service centres in the city. Due to a large number of villages and limited time and costs, not all villages can be served, so the kNN algorithm is needed to determine which villages are eligible to be served. The criteria used in this determination are village distance, difficulty level, and document ownership (Birth Certificate, KIA, family card, and KTPel). The classes that will be determined are "Very eligible", "Eligible", and "Not eligible". By applying Z-Score normalization with the value of K=5, the classification gets 94.12% accuracy, while non-normalized only gets 88.24% accuracy. Thus, applying normalization to training data can improve the kNN algorithm's accuracy in determining eligible villages for "ball pick-up" or mobile services.
Fourier Descriptor on Lontara Scripts Handwriting Recognition Fitriyani Umar; Herdianti Darwis; Purnawansyah Purnawansyah
ILKOM Jurnal Ilmiah Vol 15, No 1 (2023)
Publisher : Prodi Teknik Informatika FIK Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v15i1.1040.193-200

Abstract

Hal yang kritis dalam proses pengenalan pola adalah ekstraksi fitur. Merupakan suatu metode untuk mendapatkan ciri-ciri suatu citra (image) sehingga dapat dikenali satu sama lain. Pada penelitian ini, metode deskriptor Fourier digunakan untuk mengekstraksi pola aksara Lontara yang terdiri dari 23 huruf. Deskriptor Fourier adalah metode yang digunakan dalam pengenalan objek dan pemrosesan citra untuk merepresentasikan bentuk batas segmen citra. Pengenalan karakter dilakukan dengan menggunakan jarak Euclidean dan Manhattan. Hasil pengujian menunjukkan bahwa tingkat pengenalan tertinggi mencapai akurasi 91,30% dengan menggunakan koefisien Fourier sebesar 50. Pengenalan huruf menggunakan Manhattan dan Euclidean cenderung sama atau menghasilkan akurasi yang cenderung serupa. Akurasi tertinggi dicapai saat menggunakan Manhattan sebesar 91,30%.
The Satisfaction Level Analysis of the SIKOJA Application’s Users in Jambi City during the COVID-19 Pandemic Dodi Al Vayed; Ulung Pribadi; Riri Maria Fatriani
ILKOM Jurnal Ilmiah Vol 15, No 1 (2023)
Publisher : Prodi Teknik Informatika FIK Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v15i1.1284.144-152

Abstract

The purpose of this study was to prove the researcher's hypothesis, which was related to the satisfaction level analysis of the SIKOJA application’s users in Jambi City during the COVID-19 pandemic. Discussing the use of applications in the era of the COVID-19 pandemic. Optimal use of Information and Communication Technology resources allows the government to implement new ways of running information services to the fullest. This study used quantitative methods with data sources from questionnaires via google form with 93 respondents.  Data management was carried out using SEM-pls. This study used the PICIES Framework theory to determine the factors that influenced people in using SIKOJA sensitive applications. The measured variables were performance, efficiency, information, service, and control. The results of this study indicated that the value of R square was .738, the satisfaction level of using the application was 73.8%, which the R-square identified was in the medium category. Variables that influenced users of the Jambi City SIKOJA application were performance, efficiency, information, service, and control.
Classification of Engineering Journals Quartile using Various Supervised Learning Models Nastiti Susetyo Fanany Putri; Aji Prasetya Wibawa; Harits Ar Rasyid; Anik Nur Handayani; Andrew Nafalski; Edinar Valiant Hawali; Jehad A.H. Hammad
ILKOM Jurnal Ilmiah Vol 15, No 1 (2023)
Publisher : Prodi Teknik Informatika FIK Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v15i1.1483.101-106

Abstract

In scientific research, journals are among the primary sources of information. There are quartiles or categories of quality in journals which are Q1, Q2, Q3, and Q4. These quartiles represent the assessment of journal. A classification machine learning algorithm is developed as a means in the categorization of journals. The process of classifying data to estimate an item class with an unknown label is called classification. Various classification algorithms, such as K-Nearest Neighbor (KNN), Naïve Bayes, and Support Vector Machine (SVM) are employed in this study, with several situations for exchanging training and testing data. Cross-validation with Confusion Matrix values of accuracy, precision, recall, and error classification is used to analyzed classification performance. The classifier with the finest accuracy rate is KNN with average accuracy of 70%, Naïve Bayes at 60% and SVM at 40%. This research suggests assumption that algorithms used in this article can approach SJR classification system.
Evaluating The Application of Library Information System Technology using the PIECES Method in Remote Areas Anton Yudhana; Herman Herman; Suwanti Suwanti; Muhammad Kunta Biddinika
ILKOM Jurnal Ilmiah Vol 15, No 2 (2023)
Publisher : Prodi Teknik Informatika FIK Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v15i2.1539.250-261

Abstract

Over five years, the implementation of the library information system at IKIP Muhammadiyah Maumere faced a challenge, frequent errors during data input that hindered users from fully utilizing the system. These issues not only affected users’ interest but also highlighted the significance of the human factor in shaping the quality of an information system. To make full use of the system, it was crucial to identify and address the problems associated with it. This research delved into the experiences of 242 library information system users, including lecturers, students, and librarians, by using the PIECES method. The goal was to analyze users’ satisfaction and uncover any underlying issues within the system. The results of the PIECES analysis revealed average satisfaction scores, showcasing users' contentment with the system's performance (3.77), information (3.79), economy (3.80), control (3.77), efficiency (3.77), and service (3.89). These findings suggest that the library information system has been meeting users' expectations. However, a significant problem emerged in the performance variable, particularly in the system stability. Additionally, issues related to data compatibility, duplication in storage, and users’ authority management, access control, and system errors were observed in the information and control variables. Based on these identified challenges, recommendations for system improvement were made by targeting low satisfaction levels. Proposed solutions involve enhancing data management, storage practices, user access control, and reducing the risk of system errors, ensuring more efficient and reliable library information system
Building The Prediction of Sales Evaluation on Exponential Smoothing using The OutSystems Platform Sasa Ani Arnomo; Yulia Yulia; Ukas Ukas
ILKOM Jurnal Ilmiah Vol 15, No 2 (2023)
Publisher : Prodi Teknik Informatika FIK Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v15i2.1529.222-228

Abstract

To get a large profit in a company or business is to determine sales predictions for the next period. Prediction or forecasting is one of the keys to the success of sales because the predicted value of sales can be used as a reference to determine the order of goods, so there is no loss. Exponential smoothing method is a fairly superior forecasting method in long-term, medium-term and short-term forecasting. The data to be processed is sales data for the 2020-2022 period. The single exponential smoothing method was chosen because it can determine sales predictions for the next period with the smallest error value. The evaluation method used is MAPE, ME, MAD and MSE where this forecasting method is used to find the smallest error value. Based on the calculation results, the smallest error value obtained is ME at 62.8, MAD at 179.9, MSE at 55564.5, and MAPE at 9.20%. The value is at alpha 0.3. The next stage is to design a prediction system using the out-systems platform version 11.14.1 as a place to design the system. The test results of the system that has been designed to assist business owners in making decisions on product inventory estimates.
Combination of the MADM Model Yager and k-NN to Group Single Tuition Payments Alders Paliling; Muh Nurtanzis Sutoyo
ILKOM Jurnal Ilmiah Vol 15, No 2 (2023)
Publisher : Prodi Teknik Informatika FIK Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v15i2.1349.326-334

Abstract

Tuition payments at State Universities (PTN) use a Single Tuition Fee (UKT) payment system. It has been  implemented to make it easier for students to pay their tuition. The UKT system is divided into several groups starting from the UKT group I  to VIII. Universitas Sembilanbelas November (USN) Kolaka  is a state university and the university should determine the amount of tuition fees for each student according to the UKT system. In determining the UKT group for each student, several variables were used to make it easier to group student into their UKT groups. However, the large number of students, a number of variables and the limited time to determine the amount of UKT for each student become an issue,  so a method was needed to help USN Kolaka in grouping UKT for each student. One thing that can be done was to use the MADM model Yager and k-NN in order to make it easier to group UKT students. The results of the study showed that the use of the MADM Model Yager and k-NN could determine the UKT group of the students, and the results obtained for the UKT group I were 63 people (21.95%), the UKT group II were 72 people (25.09%), the UKT group III were 120 people (41.81%), UKT group IV were 7 people (2.44%), and UKT group V were 25 people (8.71%).
Rectified Linear Units and Adaptive Moment Estimation Optimizer on ANN with Saved Model Prediction to Improve The Stock Price Prediction Framework Performance Sekhudin Sekhudin; Yuli Purwati; Fandy Setyo Utomo; Mohd Sanusi Azmi; Pungkas Subarkah
ILKOM Jurnal Ilmiah Vol 15, No 2 (2023)
Publisher : Prodi Teknik Informatika FIK Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v15i2.1586.271-282

Abstract

A stock is a high-risk, high-return investment product. Prediction is one way to minimize risk by estimating future prices based on past data. There are limitations to solving the stock prediction problem from previous research: limited stock data, practical aspects of application, and less than optimal stock price prediction results. The main objective of this study is to improve the prediction performance by formulating and developing the stock price prediction framework. Furthermore, the research provides a stock price prediction framework that can produce better prediction results than the previous study with fast computation time. The proposed framework deals with data generation, pre-processing and model prediction. In further, the proposed framework includes two prediction methods for predicting stock closing prices: stored model prediction and current model prediction. This study uses an artificial neural network with Rectified Linear Units as an activation function and Adam Optimizer to predict stock prices. The model we have built for each forecasting method shows a better MAPE value than the model in previous studies. Previous research showed that the lowest MAPE was 1.38% for TLKM shares and 0.81% for BBRI. Our proposed framework based on the stored model prediction method shows a MAPE value of 0.67% for TLKM shares and 0.42% for BBRI. While the current model prediction method shows a MAPE value of 0.69% for TLKM shares and 0.89% for BBRI. Furthermore, the stored model prediction method takes 1.0 seconds to process a single prediction request, while the current model prediction takes 220 seconds.
Naïve Bayes and K-Nearest Neighbor Algorithm Approach in Data Mining Classification of Drugs Addictive Diseases Dadang Priyanto; Ahmad Robbiul Iman; Deny Jollyta
ILKOM Jurnal Ilmiah Vol 15, No 2 (2023)
Publisher : Prodi Teknik Informatika FIK Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v15i2.1544.262-270

Abstract

Indonesia, with its very large population, is a potential market for drugs trafficking. Hence, seriousness is needed in cracking down or preventing drug trafficking. Narcotics are substances or drugs that can cause dependence or addicted and other negative impacts on users. The problem is that drug users do not realize and even ignore diseases caused by drug addiction. The diseases can be life-threatening for users, such as inflammation of the liver, heart disease, hypertension, stroke, and others. The prevalence rate of drug abuse in West Nusa Tenggara (NTB) is included in the high category, reaching 292 cases or around 37.24% cases. This study aimed to create an application that can classify various diseases of drug users using the naïve bayes and KNN methods. The results of this study indicated that there was a very close relationship between drug users and various deadly diseases. The prediction results showed that the naive bayes method provided a prediction accuracy of 94.5% while the KNN showed a prediction accuracy of 92.5%. This shows that the naive bayes method provides better predictive performance than the KNN in the data set of drug addicts in NTB.

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