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IJISTECH (International Journal Of Information System & Technology)
Published by STIKOM Tunas Pematang Siantar
ISSN : 25807250     EISSN : -     DOI : -
Core Subject : Science,
Computer Science & IT
IJISTECH (International Journal Of Information System & Technology) is published with both online and print versions. The journal covers the frontier issues in the computer science and their applications in business, industry and other subjects. The computer science is a branch of engineering science that studies computable processes and structures. It contains theories for understanding computing systems and methods; computational algorithms and tools; methodologies for testing of concepts. The subjects covered by the journal include artificial intelligence, bioinformatics, computational statistics, database, data mining, financial engineering, hardware systems, imaging engineering, internet computing, networking, scientific computing, software engineering, and their applications etc.
Arjuna Subject : -
Articles 394 Documents
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Expert System for Detecting Diseases in Cattle Using Backward Chaining Method Ramadani, Putri; Wahyuni, Alvi Dwi; Putra, Eka Ramadhani
IJISTECH (International Journal of Information System and Technology) Vol 8, No 6 (2025): The April edition
Publisher : Sekolah Tinggi Ilmu Komputer (STIKOM) Tunas Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/ijistech.v8i6.383

Abstract

Cow is one of the livestock animals with commercial or economic value due to the sale of beef and bull semen. Livestock diseases can reduce the quality of livestock and cause a decline in sales. This research aims to help farmers recognize or identify types of diseases in cows based on visible symptoms or to prevent the risk of disease to avoid outbreaks. All data used comes from experts and a collection of documents from magazines and books related to livestock diseases. This analysis applies backward chaining in expert systems, particularly systems that process existing facts to reach conclusions. Facts are derived from physical conditions, also called symptoms. Backward chaining is a goal-based analysis that starts with an assumption of what might happen, then searches for facts (evidence) or symptoms that support (or refute) the hypothesis. The development of a web-based expert system makes it easier for farmers to access the system online. The accuracy of the expert system has been tested by stakeholders or experts, resulting in fast, accurate, and effective information. This research can assist farmers in diagnosing symptoms in livestock, and the test results can accurately detect the type of disease in livestock so that treatment can be carried out quickly.
Optimization of Mobile Attendance System with Haversine Formula Method for Field Work Practice Students Alkodri, Ari Amir; Fitriyani, F; Isnanto, Burham; Sari, Melati Suci Maya; Andrika, Yuyi
IJISTECH (International Journal of Information System and Technology) Vol 8, No 6 (2025): The April edition
Publisher : Sekolah Tinggi Ilmu Komputer (STIKOM) Tunas Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/ijistech.v8i6.378

Abstract

Conventional attendance of Field Work Practice (PKL) students still has various obstacles, such as manipulation of attendance, late recording, and difficulties in validating accurate attendance. In today's digital era, the use of location-based technology is a solution to overcome these problems. Therefore, this study aims to develop and optimize a location-based mobile attendance system by implementing the Haversine Formula Method. This method is used to calculate the distance between the student's GPS coordinates and the predetermined PKL location, so that the system only allows attendance if the student is within a certain radius.With implementation on the Android platform. The main features of this system include real-time location recording, automatic validation based on GPS coordinates, and encrypted data security. Accuracy measurement uses a comparison of the Haversine distance calculation results with a mapping application. The results of the study show that the Haversine Formula method is able to calculate distances with high accuracy, and the system developed can prevent cheating in attendance. Thus, the application of this method can improve transparency, efficiency, and accuracy of recording the attendance of PKL students in real-time, provide convenience for institutions in monitoring the presence of coordinates laboratory location accurately with a distance of ≤ 50 meters, so that the system can determine whether students are within the radius permitted to take attendance at Latitude: -2.123900 and Longitude: 106.788800. at the location of the PKL institution, namely in the ISB Atma Luhur computer laboratory.
The Influence of Ease of Use and Utilization of the E-Stock Application on the Effectiveness of the Learning Process Using the EUCS Method (Case SMKN 10 Samarinda) Mumek, Trifena Oliviana; Yunita, Y; Yulindawati, Y
IJISTECH (International Journal of Information System and Technology) Vol 9, No 1 (2025): The June Edition
Publisher : Sekolah Tinggi Ilmu Komputer (STIKOM) Tunas Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/ijistech.v9i1.388

Abstract

The management of inventory items in vocational school environments often faces administrative challenges, such as manual record-keeping that is prone to errors and process delays. SMKN 10 Samarinda implemented the E-Stock application as an information system solution to address these issues. This study aims to evaluate user perceptions of the ease of use and utilization of the E-Stock application, as well as its impact on the effectiveness of use in school operational activities and in supporting the efficiency of the learning process, which depends on the availability of facilities and infrastructure. This research employs a descriptive quantitative approach, with data collected through a closed-ended questionnaire distributed to nine active respondents. The instrument consists of 18 statements representing three main variables: ease of use, utilization, and effectiveness. Each item was rated using a Likert scale ranging from 1 to 5. Data were analyzed descriptively using SPSS to obtain the average score for each variable studied. The analysis results show that ease of use received an average score of 4.37, utilization scored 4.40, and effectiveness scored 4.28. All three scores fall into the high category, indicating that users have a very positive perception of the application. Based on these results, it can be concluded that the E-Stock application has met user expectations in supporting work efficiency and school administration, as well as contributing to the smooth running of the learning process through improved inventory management. This study also affirms that evaluation models such as EUCS are relevant to be used in the context of educational information systems.
Evaluating Urban Green Space Dynamics in Makassar City Through NDVI-Based Analysis of Sentinel-2 Imagery Edra, Anggun Purnama; Azzahra, Cantika; Nurhayati, Shifa
IJISTECH (International Journal of Information System and Technology) Vol 9, No 1 (2025): The June Edition
Publisher : Sekolah Tinggi Ilmu Komputer (STIKOM) Tunas Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/ijistech.v9i1.392

Abstract

Urban expansion in rapidly growing cities like Makassar has considerable implications for vegetation loss and ecosystem degradation. This study investigates vegetation cover changes in Makassar City by analyzing the Normalized Difference Vegetation Index (NDVI) derived from Sentinel-2 imagery for the years 2020 and 2024. A threshold of NDVI > 0.5 was applied to identify areas classified as dense vegetation. Image pre-processing, NDVI computation, and binary classification were performed to quantify and map vegetation extent. The analysis reveals a noticeable reduction in densely vegetated areas. This study analyzes the changes in vegetation health in Makassar City using the Normalized Difference Vegetation Index (NDVI) for the years 2020 and 2024. The NDVI threshold of >0.5 was used to identify areas of healthy vegetation. The spatial analysis and classification maps reveal a significant decline in vegetated areas, with a decrease from 4,792.93 hectares in 2020 to 1,157.23 hectares in 2024. This trend highlights a substantial reduction in healthy vegetation cover, potentially caused by urban development, land-use changes, and environmental pressures. The findings underscore the need for sustainable land management and green infrastructure policies to mitigate the adverse effects of vegetation loss and promote ecological balance in urban areas.
Analysis of the Potential and Business Development Opportunities in Catfish Farming Using Artificial Neural Networks Rianda, Kiki Rizki; Apdillah, Dicky
IJISTECH (International Journal of Information System and Technology) Vol 8, No 6 (2025): The April edition
Publisher : Sekolah Tinggi Ilmu Komputer (STIKOM) Tunas Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/ijistech.v8i6.384

Abstract

In recent years, there has been a significant increase in catfish consumption. The average consumer demand reaches 50 to 100 kg with a catfish harvest age of about 2.5 months. Catfish farming has not only increased the income of the community but has also transformed those who previously had no knowledge of how to farm catfish and the potential of utilizing yard land into successful catfish farmers. In connection with this, the author intends to recognize more deeply the potential and opportunities of catfish farming in Air Hitam Village, Kualuh Leidong District. In this research, the author applies the Learning Vector Quantization (LVQ) method, which is one of the approaches in Artificial Neural Networks. Learning Vector Quantization (LVQ) is a competitive layer training technique with a supervised learning approach, which uses a network structure with a single layer. The use of Artificial Neural Network (ANN) is a sophisticated way that can be applied to manage catfish farming business. The results showed that the use of the LVQ method in analyzing catfish farming data can help farmers make more informed decisions, predict business development, and increase yields and profits.
Application of Large Language Model for New Student Admission Chatbot Anwar, Rafidan; Pratiwi, Heny; Wahyuni, W
IJISTECH (International Journal of Information System and Technology) Vol 8, No 6 (2025): The April edition
Publisher : Sekolah Tinggi Ilmu Komputer (STIKOM) Tunas Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/ijistech.v8i6.379

Abstract

This study aims to develop a chatbot system based on a Large Language Model (LLM) that provides information related to new student admission in higher education. The system utilizes the SentenceTransformer model to generate embeddings of question and answer texts, as well as FAISS for vector-based search. Additionally, LLAMA is used to generate context-based answers, allowing the chatbot to provide more dynamic and relevant responses. System evaluation is conducted using ROUGE-1, ROUGE-2, and ROUGE-L metrics. The evaluation results show an average ROUGE-1 Precision of 54.89%, ROUGE-2 Precision of 47.37%, and ROUGE-L Precision of 52.72%. The Recall scores for ROUGE-1, ROUGE-2, and ROUGE-L are 89.43%, 74.08%, and 82.91%, respectively
Application of The Eoq Model to Web-Based Inventory Control Application In Companies Yurindra, Y; Wijaya, Benny
IJISTECH (International Journal of Information System and Technology) Vol 9, No 1 (2025): The June Edition
Publisher : Sekolah Tinggi Ilmu Komputer (STIKOM) Tunas Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/ijistech.v9i1.397

Abstract

Inventory of goods is one of the important components in a company. Problems occur in the management of companies that still use a manual system, so that companies have difficulty determining the stock of updated goods, recaping incoming and outgoing goods, and making routine reports. This will affect the next inventory planning and the company makes inventory with estimated needs or when goods run out without being known. To overcome this problem, an application is needed that can manage the company's inventory of goods. This study uses object-oriented methods and prototyping models. Accompanied by an inventory planning calculation model using the Economic Order Quantity model. In its design, it uses a unified modelling language, encoded with the PHP programming language, and tests are carried out with black-box testing. Inventory control applications built on the web by implementing Economic Order Quantity as inventory planning can run well, so that companies can use them to overcome inventory management and planning problems.
Customer Loyalty Classification With Random Forest Algorithm Sari, Anggi Puspita; Noviriandini, Astrid; Fauziah, Sifa
IJISTECH (International Journal of Information System and Technology) Vol 9, No 1 (2025): The June Edition
Publisher : Sekolah Tinggi Ilmu Komputer (STIKOM) Tunas Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/ijistech.v9i1.393

Abstract

Customer loyalty is very important for the survival of the company. Because with customers who have customer loyalty, they will make purchases regularly. Customer loyalty needs to be maintained to increase profits. The method is to classify loyal customers with non-loyal ones, in order to retain loyal customers and set strategies for non-loyal customers. The method used is classification with random forest with cleaning stages that can clean data from noise or empty data or data that does not match, selection that can select some data to be processed for classification, transformation that can change data into two or three formats, classification with random forest with split validation using testing data and training data and with rapidminer software. Evaluation by checking the results of the classification with random forest in the form of accuracy, precision, recall, and AUC. The results of the classification show from the accuracy table that the prediction of loyal and true loyal customers is 129 more than the prediction of not loyal and true not loyal customers which is 32. The accuracy result is 96.41% which shows that the data is really accurate with very high results. The recall result is 98.47%, while the precision result is 96.99%.
Analysis of Nutritional Needs In Elementary School-Aged Children In Remote, Underdeveloped, and Border Regions Using Android-Based Artificial Neural Network Method Febriani, Arisa; Apdillah, Dicky
IJISTECH (International Journal of Information System and Technology) Vol 8, No 6 (2025): The April edition
Publisher : Sekolah Tinggi Ilmu Komputer (STIKOM) Tunas Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/ijistech.v8i6.385

Abstract

The nutritional needs of elementary school children are very important to support their growth, development, and learning abilities. Good nutrition is essential to support the growth of bones, muscles, and organs. In addition, protein, calcium, and iron intake are very important. Nutrition also affects brain function, including children's concentration and memory. One of the schools where various factors related to children's nutrition can be studied is State Elementary School 134633 Tanjung Balai. It is hoped that the analysis of nutritional needs in school children can provide an overview of food consumption patterns, nutritional status, and the factors that influence them. The system developed using the Artificial Neural Network (ANN) model with the Backpropagation algorithm successfully analyzed the nutritional status of children based on the data provided. By categorizing nutritional status into thin, fat, and normal, the system can provide adequate results for the nutritional analysis needs of Elementary School Children in the 3T Area.
Optimization of Spareparts Stock Data Management at PT. Astra Motor Kaltim 2 using the Trend Moment Method Adeputra, James; Pratiwi, Heny; Wahyuni, W
IJISTECH (International Journal of Information System and Technology) Vol 8, No 6 (2025): The April edition
Publisher : Sekolah Tinggi Ilmu Komputer (STIKOM) Tunas Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/ijistech.v8i6.380

Abstract

Spareparts inventory management is a crucial aspect of operations in automotive companies, including PT. Astra Motor Kaltim 2. An imbalance between demand and spareparts availability can lead to stockpiling or stock shortages, ultimately resulting in operational cost inefficiencies. Therefore, this study aims to analyze and forecast spareparts sales using the Trend Moment method to optimize stock management. The Trend Moment method is used to identify sales trend patterns for sparepart 44711K59A12, based on historical sales data from September 2024 to February 2025. The forecasted results are then adjusted using a seasonal index to improve accuracy. Forecast accuracy is evaluated using the Mean Absolute Percentage Error (MAPE), which provides an overview of how close the forecasted results are to the actual data. The results of the study show that the Trend Moment method can provide fairly accurate predictions in estimating the demand for sparepart 44711K59A12 in the upcoming periods. By implementing this method, the company can develop a more efficient stock procurement strategy, reduce the risk of overstocking or stockouts, and improve customer satisfaction. In conclusion, this forecasting approach can serve as a solution to enhance the effectiveness of spareparts inventory management at PT. Astra Motor Kaltim 2

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