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All Journal Techno.Com: Jurnal Teknologi Informasi Jurnal Teknologi Informasi dan Ilmu Komputer Journal of Information Technology and Computer Science Knowledge Engineering and Data Science InComTech: Jurnal Telekomunikasi dan Komputer JOURNAL OF APPLIED INFORMATICS AND COMPUTING TEKTRIKA - Jurnal Penelitian dan Pengembangan Telekomunikasi, Kendali, Komputer, Elektrik, dan Elektronika JOISIE (Journal Of Information Systems And Informatics Engineering) JISKa (Jurnal Informatika Sunan Kalijaga) CICES (Cyberpreneurship Innovative and Creative Exact and Social Science) Community Development Journal: Jurnal Pengabdian Masyarakat Jurnal Teknologi Informatika dan Komputer Jurnal Teknik Informatika (JUTIF) Jurnal Restikom : Riset Teknik Informatika dan Komputer JINAV: Journal of Information and Visualization Jurnal Pendidikan dan Teknologi Indonesia Engineering, Mathematics and Computer Science Journal (EMACS) Jurnal Indonesia : Manajemen Informatika dan Komunikasi Jurnal Pengabdian Masyarakat Bhinneka Journal of Training and Community Service Adpertisi Prosiding Seminar Nasional Pengabdian Kepada Masyarakat Journal of Artificial Intelligence and Digital Business Journal of World Future Medicine, Health and Nursing Jurnal Indonesia : Manajemen Informatika dan Komunikasi The Journal of Enhanced Studies in Informatics and Computer Applications International Journal of Computer Science and Information Technology
Mochamad Anshori
Institut Teknologi Sains Dan Kesehatan RS Dr. Soepraoen Kesdam V/BRW
Agriculture, Biological Sciences & Forestry Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Medicine & Pharmacology Nursing Public Health Veterinary Other

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Journal : The Journal of Enhanced Studies in Informatics and Computer Applications

 1 
Use Discriminant Analysis to Identify Eroticism-Related Terms in The Lyrics of Dangdut Songs Herry Wahyu Wibowo; Muhammad Hasbi; Mochammad Anshori
Journal of Enhanced Studies in Informatics and Computer Applications Vol. 1 No. 1 (2024): JESICA Vol. 1 No. 1 2024
Publisher : Institut Teknologi, Sains, dan Kesehatan RS.DR. Soepraoen Kesdam V/BRW

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47794/jesica.v1i1.5

Abstract

The song "Dangdut" is one of the most popular songs in Indonesia, having gained popularity from the 1960s until the present. It's even been acknowledged as authentic Indonesian music. There are both positive and negative effects on the pendengarnya of lagu dangdut. Positive dampening can lower stress levels, and negative dampening occurs when emotions are heightened. If this was brought up by a young child who was not yet fully grown, it would give them a hard time and negatively impact their journey. According to this framework, it is recommended that any eroticism in the lyrics of dance music be identified. It is therefore advised to look for signs of sexuality in the lyrics of dangdut songs. The intention is to restrict and filter the music that kids can listen to. Using LDA and QDA classifiers in conjunction with natural language processing is the suggested approach. According to research findings, LDA can identify more than QDA. The LDA examination yielded the following results: recall = 56.522%, accuracy = 56.522%, precision = 79.13%, and F1score = 65.942%. It has been demonstrated that discriminant analysis, particularly LDA, is useful for classification, as QDA has not shown itself to be the most effective method in this instance.
Estimate Suicidal Rate in Indonesian based on Time Window using Linear Regression Javier Fajri Zachary; Mochammad Anshori; Wahyu Teja Kusuma
Journal of Enhanced Studies in Informatics and Computer Applications Vol. 1 No. 2 (2024): JESICA Vol. 1 No. 2 2024
Publisher : Institut Teknologi, Sains, dan Kesehatan RS.DR. Soepraoen Kesdam V/BRW

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47794/jesica.v1i2.7

Abstract

The global phenomenon of suicide should be a serious source of worry. Suicide rates are still relatively high in Indonesia. According to the research, there are many different reasons why people commit suicide. Anyone can commit suicide, whether they are young children, teenagers, or adults. Preventive action is one way to avoid this. The prevalence of suicide can be used to gauge the level of preventive action. You may gauge how active you are in implementing the most effective prevention by looking at the predicted suicide rate in the future. Linear regression is one technique for predicting suicide rates. The time window (tw) method is also used to prepare the data because it is in time series form. The best regression model was tw = 5 with MSE = 0.001147, RMSE = 0.033869, and R2 = 0.981643 obtained for all rates. The model with tw = 3, which has errors of MSE = 0.001547, RMSE = 0.039334, and R2 = 0.969458, is the most accurate one for the female rate. Finally, with errors MSE = 0.00318, RMSE = 0.056392, and R2 = 0.973341, we arrive at tw = 5 for the male rate
Logistic Regression's Effectiveness in Feature Selection with Information Gain in Predicting Heart Failure Patients Mochammad Anshori; M. Syauqi Haris; Arif Wahyudi
Journal of Enhanced Studies in Informatics and Computer Applications Vol. 1 No. 2 (2024): JESICA Vol. 1 No. 2 2024
Publisher : Institut Teknologi, Sains, dan Kesehatan RS.DR. Soepraoen Kesdam V/BRW

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47794/jesica.v1i2.8

Abstract

Heart failure is a chronic illness that obstructs blood flow, which is necessary for the body to circulate oxygen. Patients with heart failure have a poor chance of survival, as evidenced by the high death rate. The hospital's infrastructure and medical facilities determine the degree of patient safety, and the patients' medical records play a significant role in ensuring that they receive the right care. As a result, a system that uses specific data to forecast the safety of heart failure patients is required. Machine learning, a computer-based approach, is one way to get around this. The logistic regression algorithm has been used to generate predictions in earlier studies. The approach for feature selection from the dataset that is suggested in this study is information gain. You can filter features that are significant to the dataset in this way. In addition, selection can enhance machine learning efficacy by decreasing the dimensions of the data. Five features—time, serum creatinine, ejection fraction, age, and serum sodium—are the outcome of information gain. After that, predictions were made using logistic regression, and a data sharing ratio of 70% training data and 30% test data resulted in an accuracy of 0.8556. This demonstrates how feature selection with Information Gain can improve the accuracy of the logistic regression model and is a very effective method.
THE DISCRIMINANT ANALYSIS FUNCTION WAS IMPLEMENTED TO PREDICT THE PRESENCE OF DIABETES Herry Prasetyo Wibowo; Mochammad Anshori; M. Syauqi Haris
Journal of Enhanced Studies in Informatics and Computer Applications Vol. 1 No. 2 (2024): JESICA Vol. 1 No. 2 2024
Publisher : Institut Teknologi, Sains, dan Kesehatan RS.DR. Soepraoen Kesdam V/BRW

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47794/jesica.v1i2.10

Abstract

Diabetes is a condition blood sugar concentrations are high and there is something wrong with insulin inside the body. A hormone called insulin controls the equilibrium of blood sugar concentration in humans. Diabetes has high-risk health, such as CKD, CVD, skin disease or even blindness. The reason people suffer from diabetes is caused of bad consumption habits. Some symptoms of diabetes are frequent urination and feeling hungry too quickly. Diabetes is sometimes difficult to diagnose, which is why it is also referred to as the silent killer. A preventive way is an early prediction of diabetes disease. This is very important to do. In this study, the discriminant analysis algorithm is used along with machine learning techniques. In this study, machine learning techniques are used. Its name is discriminant analysis algorithm. Two popular versions are linear discriminant analysis (LDA) and quadratic discriminant analysis (QDA). This method is used because it is suitable for high-dimensional data and the discriminant analysis algorithm has minimal parameters. The discriminant analysis algorithm uses few parameters and this method appropriate for high-dimensional data. We'll compare the two approaches to find a way to demonstrate their dependability. Both approaches would be contrasted. Based on the result, QDA has the best performance. QDA can produce accuracy = 93.7%, TPR = 93.7%, precision = 94.3%, recall = 93.7% and F-measure = 93.9%. FPR of QDA is the lowest one, it is 1.02%. It means QDA has a small error in making predictions. Overall, based on the result QDA is the proven and proper method for detecting diabetes disease
Comparing Discriminant Analysis Function for Early Prediction of Smartphone Addiction Mufid Musthofa; Mochammad Anshori
Journal of Enhanced Studies in Informatics and Computer Applications Vol. 2 No. 1 (2025): JESICA Vol. 2 No. 1 2025
Publisher : Institut Teknologi, Sains, dan Kesehatan RS.DR. Soepraoen Kesdam V/BRW

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47794/jesica.v2i1.12

Abstract

The pervasive use of smartphones in daily life has led to significant benefits, but excessive use has caused alarming behavioral and health issues, particularly among adolescents. Addressing smartphone addiction requires early detection to enable timely interventions. This study investigates the application of machine learning, specifically Linear Discriminant Analysis (LDA) and Quadratic Discriminant Analysis (QDA), for the early prediction of smartphone addiction. The research used a dataset containing 394 instances categorized into "addicted" and "non-addicted" classes. Dataset is derived from questionnaire responses. After preprocessing steps, including feature selection and ordinal encoding, the data was split using 10-fold cross-validation to ensure robust evaluation. The models were assessed using metrics such as accuracy, precision, recall, and F-measure. Results indicate that LDA significantly outperforms QDA across all metrics, achieving an accuracy of 94.16%, a precision of 94.2%, a recall of 94.2%, and an F-measure of 94.2%. Additionally, the Receiver Operating Characteristic (ROC) curve analysis showed an Area Under the Curve (AUC) of 0.9875 for LDA, indicating its high reliability and stability in classifying smartphone addiction. QDA, while effective, has a slightly lower performance due to the linear separability of the dataset. This study concludes that LDA is a robust and effective method for early prediction of smartphone addiction, offering valuable insights for health monitoring systems. The findings provide a foundation for future applications of discriminant analysis in addressing behavioral health issues.
MediStock: Medical Stock Website Development Using Design Thinking Novelia Mega Puspita; Dita Kurnia Rachmasari; Naufal Alif Vivaldi; Mochammad Anshori
Journal of Enhanced Studies in Informatics and Computer Applications Vol. 2 No. 1 (2025): JESICA Vol. 2 No. 1 2025
Publisher : Institut Teknologi, Sains, dan Kesehatan RS.DR. Soepraoen Kesdam V/BRW

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47794/jesica.v2i1.25

Abstract

Pharmacies play a vital role in public health by ensuring the availability of essential medications. However, inefficient inventory management systems, particularly in Malang, lead to operational inefficiencies, stock discrepancies, and regulatory compliance challenges. This study aims to develop a web-based inventory management system, MediStock, using the Design Thinking methodology to address these issues effectively. The research employs a human-centered approach, focusing on user needs and experiences through the stages of empathize, define, ideate, prototype, and testing. The system integrates real-time stock monitoring, predictive analytics, and compliance with electronic medical records, enhancing operational efficiency and regulatory adherence. Results indicate that MediStock significantly improves inventory management by minimizing stock discrepancies, optimizing procurement processes, and ensuring real-time visibility of medicine stocks. The heuristic evaluation revealed high usability and adaptability among different user groups, confirming the system's effectiveness and the user-centered design. These findings highlight the potential of Design Thinking to bridge the gap between complex technological solutions and user needs in healthcare inventory management. This study contributes to the field by providing an innovative, user-friendly inventory management solution that enhances operational efficiency and regulatory compliance. Future research should explore the scalability of the system and its integration with broader healthcare management systems to maximize its impact on the healthcare sector.
Co-Authors ACHMAD JAELANI RUSDI Ahmad Afif Supianto Ahsanun Naseh Khudori Ali Hasan, Hildan Adisqi Anis Ansyori Ardimansyah Ardimansyah Arif Wahyudi Baskoro, Danang Bagus Choirunnisa, Rosyida Dio Wahyu Saputra Dita Kurnia Rachmasari Dyah Utami, Kartika Fita Rusdian Ikawati Geraldo, Tostes Heri Wahyu Wibowo Herman Tolle Herry Prasetyo Wibowo Herry Wahyu Wibowo Irnanda Septian Ika Putri Javier Fajri Zachary Jimmy H Moedjahedy Khofida, Nisa M. Syauqi Haris Marilia, Busatto Mufid Musthofa Muhammad Hasbi Naufal Alif Vivaldi Nindynar Rikatsih Nindynar Rikatsih Ningrum, Afifah Vera Ferencia Fitria Novelia Mega Puspita Nugroho Teguh Yuono Pradini, Risqy Siwi Prasetiyo, Bagus Dadang Putri, Irnanda Septian Ika Putrinugroho, Ivana Aikozora R. A. Rengganis Ularan Risqy Siwi Pradini Saletia, Maria Claudia Samuel PD Anantadjaya Saputra, Dio Wahyu Siwi Pradini, Risqy Susi Indriyani Wahyu Tejakusuma, Wahyu Wayan Firdaus Mahmudy Wibowo, Heri Wahyu Yahya, Furqon Nurbaril Yuono, Nugroho Teguh