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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 10 Documents
 1 
Search results for , issue "Vol 8, No 6 (2025): The April edition" : 10 Documents clear
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.
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
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
Analysis of Swiftlet Nest Quality In Relation to Price using the Sugeno Method Lubis, Fitri Handayani; Sitorus, Zunaida
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.v8i6.386

Abstract

Many swiftlet nest entrepreneurs in Pangkalan Lunang Village lack a comprehensive understanding of how to assess the quality of swiftlet nests. In fact, nest quality is influenced by various factors, including cultivation techniques, environmental conditions, and post-harvest handling. This lack of knowledge often leads to challenges in maintaining product quality, ultimately affecting selling prices and market competitiveness. Given this situation, it is essential to conduct an in-depth analysis of how nest quality influences its market value. This study employs the Fuzzy Sugeno method to help swiftlet business owners gain a clearer understanding of the relationship between quality parameters—such as color, humidity, shape, and cleanliness—and the selling price. The findings indicate that these quality attributes significantly impact the selling price; the higher the quality, the greater the market value. This emphasizes the importance of adopting best practices in cultivation and post-harvest management to enhance nest quality, meet consumer expectations, and ultimately increase profitability and competitiveness in the market.
Evaluation of the New Student Admission Website of STMIK Widya Cipta Dharma Using the End-User Computing Satisfaction Method Damaya, Filio Angga; Pratiwi, Heny; Yunita, Y
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.v8i6.381

Abstract

The New Student Admission (PMB) website is one of the platforms used in the admission and registration process for prospective students at STMIK Widya Cipta Dharma. An evaluation of the PMB website is necessary to ensure user convenience and satisfaction during the registration process. This study aims to evaluate the new student admission website of STMIK Widya Cipta Dharma using the End-User Computing Satisfaction (EUCS) method. The EUCS method consists of five main dimensions: content, accuracy, format, ease of use, and timeliness. Data was collected through questionnaires distributed to registered students, prospective students of STMIK Widya Cipta Dharma, and users of the PMB website. The analysis results indicate user satisfaction in using the PMB website.
Analysis of Fertilizer Requirements in Red Chili Cultivation Using an Artificial Neural Network Approach Marpaung, Mairani; Apdillah, Dicky; Ayyub, Muhammad Azwar Al
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.v8i6.387

Abstract

Red chili farmers on the East Coast of North Sumatra still rely on manual calculations to determine the use of NPK Biru 16 Mutiara fertilizer, often leading to inaccurate and inefficient fertilizer application. This study proposes the Backpropagation method within Artificial Neural Networks (ANN) as a solution to analyze fertilizer needs more precisely. The method enables the system to learn from historical data and plant growth patterns, providing accurate recommendations for the type and amount of fertilizer required. The implementation of ANN in this context not only enhances agricultural efficiency but also supports environmental sustainability by minimizing excessive fertilizer usage.
Comparison of Naive Bayes and C4.5 Methods with Particle Swarm Optimization on Customer Loyalty Classification Wati, Embun Fajar; Perangin-Angin, Elvi Sunita; Indriyani, Luthfi
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.v8i6.382

Abstract

The Company attaches great importance to customer loyalty for the sustainability of the Company. Loyal customers will buy many times and provide great profits. In this study, the decision tree method or C4.5 and naïve bayes were used with PSO optimization for customer classification which aims to design a strategy in decision-making towards disloyal customers. Some of the stages carried out are data load into MS. Excel, data cleaning from noise, data selection as many as 238 obtained from previous research with several attributes, including, namely age, annual income, purchase amount, region, purchase frequency, and loyalty score, as well as data transformation, namely each attribute is grouped into 2 with their own criteria, data testing by modeling data through Rapidminer, Data evaluation by examining the values of accuracy, precision, recall, and AUC. Both methods have the same accuracy value of 96.67% and the same recall value of 100%. For the precision value, there is a difference of 0.6% and the precision decision tree value is higher than the naïve Bayes which is 96.16%. As for the AUC value, it is higher naïve bayes, which is 0.922 with the difference from the decision tree of 0.059. It can be concluded that the two methods in processing customer loyalty data in this study have the same accuracy, so both methods are equally good.

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