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Jurnal Sisfokom (Sistem Informasi dan Komputer)
Published by Sekolah Tinggi Manajemen Informatika dan Komputer Atma Luhur
ISSN : 23017988     EISSN : 25810588     DOI : -
Core Subject : Science, Engineering,
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management
Jurnal Sisfokom merupakan singkatan dari Jurnal Sistem Informasi dan Komputer. Jurnal ini merupakan kolaborasi antara sivitas akademika STMIK Atma Luhur dengan perguruan tinggi maupun universitas di Indonesia. Jurnal ini berisi artikel ilmiah dari peneliti, akademisi, serta para pemerhati TI. Jurnal Sisfokom diterbitkan 2 kali dalam setahun yaitu pada bulan Maret dan September. Jurnal ini menyajikan makalah dalam bidang ilmu sistem informasi dan komputer.
Arjuna Subject : -
Articles 669 Documents
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Performance Analysis of Chicken Freshness classification using Naïve Bayes, Decision Tree, and k-NN Vannya, Regina; Hermawan, Arief
Jurnal Sisfokom (Sistem Informasi dan Komputer) Vol 12, No 3 (2023): NOVEMBER
Publisher : ISB Atma Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32736/sisfokom.v12i3.1740

Abstract

Chicken is one of the staple foods that is widely enjoyed by all. To obtain the benefits of chicken meat, the level of freshness becomes one of the main keys. In general, the level of freshness of chicken meat is divided into two classes, namely fresh and non-fresh. The difference in the level of freshness can be seen from the color changes of each class. Spoiled chicken (chicken died yesterday) is one type of meat in the non-fresh group. The widespread sale of spoiled chicken meat among the public raises doubts about choosing chicken that is suitable and unsuitable for consumption. Therefore, chicken meat freshness classification is needed to facilitate the selection of chicken meat based on color characteristics. The use of Naive Bayes Classifier algorithm in categorizing fresh and non-fresh classes is done by calculating the probability value of each image channel input. This research was conducted to compare the Naive Bayes, decision tree, and K-NN algorithms in classifying chicken meat based on color characteristics. The results of the study showed that the Naive Bayes classifier algorithm was superior to the decision tree and K-NN algorithms with an accuracy rate of 75%, precision of 79%, and recall of 65%. It is known that 27 images were predicted correctly and 9 images were predicted incorrectly out of a total 36 data. The use of a histogram in this study aims to differentiate chicken meat images from non-meat during the testing process of the model using the Naive Bayes classifier algorithm.
Macine Learning Approach in Evaluating News Labels Based on Titles: Online Media Case Study Yuranda, Rezky; Sutabri, Tata; Wahyuningsih, Delpiah
Jurnal Sisfokom (Sistem Informasi dan Komputer) Vol 12, No 3 (2023): NOVEMBER
Publisher : ISB Atma Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32736/sisfokom.v12i3.1808

Abstract

In the current digital era, information availability is abundant, and news serves as a primary source of up-to-date and reliable information for the public. However, with the increasing volume of information, a robust evaluation method is necessary to ensure accurate and dependable news labeling. This research employs a machine learning approach, utilizing three common classification algorithms: Naive Bayes, SVM, and Random Forest, to evaluate news labels based on their titles. The dataset utilized in this study is obtained from Jakarta AI Research and consists of 10,000 samples covering various news topics. Evaluation is conducted using accuracy, precision, recall, and F1-Score metrics to gain a comprehensive understanding of the classification algorithm's performance. The results of this research demonstrate that the SVM algorithm exhibits the best performance, achieving an accuracy rate of 92.92%. Random Forest follows with an accuracy rate of 91.21%, and Naive Bayes with an accuracy rate of 89.61%. These findings provide deep insights into the effectiveness of the machine learning approach in evaluating news labels based on their titles. Furthermore, the study highlights the importance of considering other evaluation metrics such as precision, recall, and F1-Score to obtain a more holistic understanding of the algorithm's performance. Further research is encouraged to involve additional classification algorithms and more diverse and extensive datasets to enhance the comprehension of news label evaluation comprehensively. Such endeavors can significantly contribute to the development of automated systems for classifying news with higher accuracy and reliability in the future
Determining Scholarship Recipients at STIT Prabumulih Using the AHP Method Christian, Andi; Ariansyah, Ariansyah; Wahyuni, Anggie Sri
Jurnal Sisfokom (Sistem Informasi dan Komputer) Vol 12, No 3 (2023): NOVEMBER
Publisher : ISB Atma Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32736/sisfokom.v12i3.1717

Abstract

In every educational institution, especially universities, there are lots of scholarships offered to students. Likewise with the Prabumulih College of Engineering (STIT Prabumulih) which has a scholarship program for its students by applying predetermined rules or criteria, for example, parents' income, parents' dependents, student achievement index scores, etc. Due to this, not all scholarship recipients who apply for scholarships will receive a scholarship. The problem faced by the campus today is in the process of winning scholarships. therefore a decision support system is needed that can assist in providing scholarship recipient recommendations. In this study the authors used the AHP method and the Expert Choice application. From the calculation results obtained by the specified criteria, the GPA of 0.389 is the highest priority weight compared to other criteria. Then, from the results of calculating student data or all alternatives, the total value of each student is obtained. It can be concluded that the one who can be recommended to get a UKT scholarship is Student A because it has the highest score, namely 16.6% of the total calculated.
Comparison of Sentiment Analysis Model for Shopee Comments on Google Play Store Hasanah, Khuswatun
Jurnal Sisfokom (Sistem Informasi dan Komputer) Vol 13, No 1 (2024): MARET
Publisher : ISB Atma Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32736/sisfokom.v13i1.1916

Abstract

The current COVID-19 pandemic has greatly changed the order of consumption and the Indonesian economy. During the health crisis that hit Indonesia, the e-commerce sector experienced very rapid development because of changes in consumer behavior that are looking for safe and comfortable shopping alternatives. During the COVID-19 pandemic, Shopee became the number 1 online shopping site in Indonesia. However, this cannot be used as a standard for user satisfaction. User satisfaction can only be measured from comments by Shopee application users through the comments and rating features provided by the Google Play Store. Therefore, to be able to find out public opinion about Shopee, a sentiment analysis of the Shopee application will be carried out which can later be used by management to develop even better applications. In this study, the dataset taken is the rating and reviews of Shopee application users on the Google Play Store using the Multinomial Naïve Bayes method, Random Forest Classifier, Logistic Regression, Support Vector Machine, K-Nearest Neighbors, and Extra Trees Classifier. This study uses 1000 comment and rating data which are processed using the Python language. The results of this study indicate that the method that has the highest level of accuracy is the Support Vector Machine algorithm with an accuracy of 88%, Extra Trees Classifier with an accuracy of 86%, Logistic Regression with an accuracy of 85%, Random Forest Classifier with an accuracy of 85%, K- Nearest Neighbors with an accuracy of 83%, and the last is Multinomial Naïve Bayes with an accuracy of 78%.
Prediction of Grade Point Average (GPA) for Students at Informatics and Computer Engineering Education – Universitas Negeri Jakarta during Online Learning Using Naive Bayes Algorithm Jannah, Miftahul; Widodo, Widodo; Ajie, Hamidillah
Jurnal Sisfokom (Sistem Informasi dan Komputer) Vol 13, No 1 (2024): MARET
Publisher : ISB Atma Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32736/sisfokom.v13i1.1958

Abstract

The transition of learning models from face-to-face to online learning has had several impacts on student learning, reflected in their academic achievements. This study aims to determine the performance of the algorithm model using data mining classification techniques in predicting the Semester Grade Point Average (GPA) of Informatics and Computer Engineering Education students, at Universitas Negeri Jakarta during online learning. The prediction employed the Naive Bayes algorithm and the dataset obtained by collecting questionnaires from 2020 and 2021 batches. The total data obtained is 155 records with 13 (thirteen) attributes in the form of 1 (one) ID attribute including NIM, 11 (eleven) regular attributes including gender, college entrance, smartphone facilities, network conditions, preferred online applications, interest in learning, learning attitudes, learning creativity, parental support, study groups, and other activities outside of lectures during online learning, and 1 (one) the label attribute namely the Semester Grade Point Average for students in 3rd and 5th semester. The evaluation of this research involved the confusion matrix and the ROC (Receiver Operating Characteristic) curve. Confusion matrix resulted in an accuracy of 75%, precision of 28.33%, and recall of 26.43%. The ROC curve resulted in an AUC value of 0.679, indicating the category of poor classification. This study also applied the SMOTE data balancing technique, leading to a confusion matrix evaluation with 88.46% accuracy, 57.43% precision, and 52.14% recall. Furthermore, the ROC curve resulted in an AUC value of 0.809 which is categorized as a Good classification.
Information Technology Security Audit at the YDSF National Zakat Institution Using the ISO 27001 Framework Kamal, Mustafa; Muhamad, Muhamad; Sudianto, Yupit; Fauzan, Muhammad Arkan; Anggito, Yuvens; Yasin, Wahid; Hermawan, Hendrik
Jurnal Sisfokom (Sistem Informasi dan Komputer) Vol 13, No 1 (2024): MARET
Publisher : ISB Atma Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32736/sisfokom.v13i1.1987

Abstract

In this era of cyber crimes, data security is an important aspect that needs special attention from an organization. This is reinforced by the ratification of Law Number 27 of 2022 on personal data security. The National Zakat Amil Institute (LAZNAS) Yayasan Dana Sosial al Falah (YDSF) as an institution with a legal entity and having data on more than 100,000 donors and partners, it also has an obligation to protect the personal data of donors and partners.  The focus of this research is to evaluate and audit information technology at the LAZNAS YDSF, especially regarding the security aspect of information technology. Evaluations and audits were carried out using the ISO 27001 framework as a standardization of information technology security at the international level. In this study, information technology audits were conducted using quantitative methods. The assessment was carried out on seven main clauses that are priorities for the LAZNAS YDSF based on management priorities: compliance clauses, risk management, policies, assets, physical and environmental management, access control, and incident management. Data were collected using a questionnaire distributed to all the LAZNAS YDSF managers and employees. Fifty-five respondents, ranging from management to staff, were involved in filling out the questionnaire, ranging from management to staff. Based on the recapitulation of answers from respondents, it was found that the risk management and access control clauses had good results, with scores of 2,727 and 2,796. The compliance and incident management clauses have scores of 2.381 and 2.53, respectively; therefore, improvement efforts need to be made. By evaluating and auditing information technology that refers to the ISO 27001 standard, it is hoped that LAZNAS YDSF can protect and maintain the confidentiality, integrity, and availability of information, and manage and control information security risks.
Data-Driven Strategies for Fuel Distribution in Indonesia: A Case Study of PT Pertamina Patra Niaga Tiarazahra, Kania Lovia; Ambarwati, Rita
Jurnal Sisfokom (Sistem Informasi dan Komputer) Vol 13, No 1 (2024): MARET
Publisher : ISB Atma Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32736/sisfokom.v13i1.2030

Abstract

Fuel oil or what is often referred to as BBM is one of the basic needs to drive all community activities. So the government appointed PT Pertamina as a single company which is a state-owned company to facilitate fuel needs for all levels of society. However, with increasing demand, the government formed a new policy to allow private companies to come in to meet all fuel demand. With this, PT Pertamina is no longer the only fuel supplier in Indonesia and must continue to develop mature strategies so that profits do not fade. One way is by examining sales data and predicting customer loyalty. The RFM method followed by the decision tree algorithm and k-means clustering is applied in this research, with the output being able to determine the level of customer loyalty, the level of salesman performance, as well as predicting the potential for customers to churn and its correlation with the salesman's skills. The data used as a reference for the research is sales transaction data obtained from PT Pertamina Patra Niaga Regional Jatimbalinus. And from the research, results showed that the majority of PT Pertamina Patra Niaga Regional Jatimbalinus customers are loyal customers. With a salesman, performance is divided into good performance and less good performance. This grouping is obtained based on the salesman's overall performance track record. As for customer churn predictions, it was found that there was 1 group of customers who were predicted to churn heavily, but this was not influenced by salesman performance, as evidenced by transaction track records in existing data
Sentiment Analysis of Google Play Store User Reviews on Digital Population Identity App Using K-Nearest Neighbors Kurniawan, Rudi; Wijaya, Harma Oktafia Lingga; Aprisusanti, Rani Purnama
Jurnal Sisfokom (Sistem Informasi dan Komputer) Vol 13, No 2 (2024): JULY
Publisher : ISB Atma Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32736/sisfokom.v13i2.2071

Abstract

The Digital Population Identity Application provides convenience for users to access and manage their population data digitally. Based on the increasing usage of the Digital Population Identity Application on the Google Play Store, various user reviews of the application have emerged. Therefore, sentiment analysis is needed to provide a deeper understanding of user perceptions and to classify user reviews of the Digital Population Identity Application. Sentiment analysis is a computational study of opinions, feelings, and emotions expressed in text, using the K-Nearest Neighbors method, which is a classification method based on the closest distance or similarity to objects in the training data. Using 5000 relevant review data from September 2022 to December 2023, after labeling them into positive, negative, and neutral sentiment classes, the results show 3581 negative sentiments, 1031 positive sentiments, and 388 neutral sentiments. Testing was conducted by applying the K-Nearest Neighbors method in the classification stage, testing this method by varying K values from 1 to 10. The best results were obtained with a training data ratio of 90% to testing data ratio of 10%. The best results were achieved at K values of 8, 9, and 10, with an accuracy of 81%, precision of 82%, recall of 95%, and an F1-Score of 88%. With a training data ratio of 70% to testing data ratio of 30%, the best results were obtained at K values of 6, 7, 8, 9, and 10, with an accuracy of 80%, precision of 81%, recall of 95%, and an F1-Score of 88%. Based on the results of this research, the K-Nearest Neighbors method can be used for sentiment classification of user reviews with good results.
Sensitivity Analysis of Various AHP Process: A Case Study on Consumption Fish Farming Michael Siregar, Ivan; Budi Putri, Lydia Wulandari; Sugihartono, Tri
Jurnal Sisfokom (Sistem Informasi dan Komputer) Vol 13, No 2 (2024): JULY
Publisher : ISB Atma Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32736/sisfokom.v13i2.2101

Abstract

The utilization of a decision support system has successfully helped many businesses in increasing their product sales. By conducting product evaluations, the sales potential of each product will be seen more accurately, thereby helping strategic decision-makers. As one of the algorithms in product selection, AHP  has been proven to solve complex problems involving multi-criteria, as many studies have successfully used it to rank products. However, in AHP implementation there are two different ways of calculating weights and consistency ratios. Due to the various AHP processes available, this paper performs testing with the most frequently used variations to determine product potential and compare the methods for multi-criteria decision-making. The criteria are harvest duration, selling price, feed production, weather conditions, and target market. The research results show that the weights of the two methods are different, but the resulting ranks are the same. The best choice type of fish to be farmed by fish farmers is catfish with the highest weight and the most difficult type of fish to farm is giant gourami. The result also show that the best way of the normalization process is squares of comparison matrices because its sensitivity does not easily change the ranking order.
Comparative Analysis: Machine Learning Algorithms for TOC Prediction in Pharmaceutical Water Treatment Systems Mustapa, Dieki Rian; Tjahyanto, Aris
Jurnal Sisfokom (Sistem Informasi dan Komputer) Vol 13, No 2 (2024): JULY
Publisher : ISB Atma Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32736/sisfokom.v13i2.2148

Abstract

Water quality is crucial in pharmaceutical production, where it serves as a solvent and raw material. Contamination with organic compounds poses a risk to product integrity and safety. TOC serves as a key indicator for assessing organic pollution levels in water. An increase in TOC signals potential issues with water treatment systems. Machine learning prediction of TOC values is essential for preemptive monitoring and maintenance. This study aimed to compare three different machine learning algorithms - Linear Regression (RL), Random Forest (RF), and multilayer perceptron (MLP) - for predicting Total Organic Carbon (TOC) in pharmaceutical water treatment systems. By utilizing a dataset covering various operational conditions of pharmaceutical water treatment systems, the research conducted a comprehensive analysis. Each algorithm underwent evaluation using performance metrics like coefficient of determination (R-squared), and prediction accuracy to assess their effectiveness in predicting TOC concentrations. A correlation coefficient approaching 1 (100%) signifies a strong relationship between model predictions and actual target values (accuracy prediction), while a smaller Mean Absolute Error (MAE) indicates higher accuracy in predicting target values. The study found that the results of the correlation coefficient in order from highest to lowest are the RF, MLP, and RL models with values of 95.04%, 93.11%, and 80.27%, respectively. Likewise, additional metrics for evaluation, including Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), Relative Absolute Error (RAE) and Root Relative Squared Error (RRSE), exhibit a ranking from lowest to highest values across RF, MLP, and RL models. RF has a higher prediction accuracy of the TOC than other models (95%) and lowest MAE (3.9). This research offers valuable insights into utilizing machine learning algorithms for TOC prediction within pharmaceutical water treatment to make informed decisions, improving water treatment systems and overall product quality.

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