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Anjar Wanto
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JOMLAI: Journal of Machine Learning and Artificial Intelligence
Published by Yayasan Literasi Sains Indonesia
ISSN : 28289102     EISSN : 28289099     DOI : 10.55123/jomlai
Core Subject : Science, Engineering,
Computer Science & IT Engineering
Focus and Scope JOMLAI: Journal of Machine Learning and Artificial Intelligence is a scientific journal related to machine learning and artificial intelligence that contains scientific writings on pure research and applied research in the field of machine learning and artificial intelligence as well as an overview of the development of theories, methods, and related applied sciences. Topics cover the following areas (but are not limited to): Software engineering Hardware Engineering Information Security System Engineering Expert system Decision Support System Data Mining Artificial Intelligence System Computer network Computer Engineering Image processing Genetic Algorithm Information Systems Business Intelligence and Knowledge Management Database System Big Data Internet of Things Enterprise Computing Machine Learning Other relevant study topics Noted: Articles have primary citations and have never been published online or printed before
Arjuna Subject : Ilmu Komputer (semua kategori) - Kecerdasan Buatan
Articles 82 Documents
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Utilization of the C4.5 Algorithm for Classifying Students Who Are Eligible to Apply for Obtaining PIP Fazira, Rizky Nazwa; Wanto, Anjar; Gunawan, Indra
JOMLAI: Journal of Machine Learning and Artificial Intelligence Vol. 2 No. 2 (2023): June
Publisher : Yayasan Literasi Sains Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55123/jomlai.v2i2.3187

Abstract

This research aims to optimize the selection process for eligible students for the Smart Indonesia Program (PIP) using the C4.5 algorithm. PIP is a social assistance program aimed at students who meet specific criteria. The C4.5 algorithm was chosen because it can produce a decision model that can be used to classify students based on various assessment factors. This study involved collecting student data based on predetermined PIP criteria. This data is then used as input to train the model using the C4.5 algorithm. The training involves identifying patterns and relationships between significant variables in determining a student's eligibility for PIP. This research resulted in a decision regarding PIP (Smart Indonesia Program) recipients with a low parental income classification who were entitled to assistance. It is hoped that the results of this research can contribute to increasing efficiency and accuracy in the selection process for PIP recipient students. Using the C4.5 algorithm is expected to produce more objective decisions and identify students who qualify for assistance more precisely. Apart from that, this research can also provide new insights regarding implementing algorithms in the context of education policy and social assistance.
Analysis of Family Economic Factors on Students' Learning Interest Using the C4.5 Algorithm Rahayu, Dian; Solikhun, Solikhun; Sormin, Rizky Kairunnisa
JOMLAI: Journal of Machine Learning and Artificial Intelligence Vol. 2 No. 2 (2023): June
Publisher : Yayasan Literasi Sains Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55123/jomlai.v2i2.3195

Abstract

This study aims to analyze the influence of family economic factors on the learning interest of students at SMA Negeri 2 Pematangsiantar using the C4.5 algorithm. The C4.5 method is employed to identify the relationship between family economic variables and students' learning interest. The research is conducted at SMA Negeri 2 Pematangsiantar, involving students as the main respondents. Data is collected through a questionnaire covering family economic variables and the level of students' learning interest. Data analysis using the C4.5 algorithm assists in identifying family economic factors that significantly affect students' learning interest. The study's results are expected to provide a deeper understanding of the impact of family economic factors on student learning motivation in the high school environment. This research contributes to the education literature and offers insights for educators, parents, and education stakeholders to enhance support for students with diverse family economic backgrounds. The implications of these findings can aid in designing more inclusive education policies and supporting academic growth for high school students.
Diagnosis of Skin Diseases Using Artificial Neural Networks with Backpropagation Algorithm Dony Jordan Pangomoan Sirait; Angga Priandi; Yemima Pepayosa Sembiring; Alyah Octafia; Victor Asido Elyakim P
JOMLAI: Journal of Machine Learning and Artificial Intelligence Vol. 4 No. 1 (2025): Maret 2025
Publisher : Yayasan Literasi Sains Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55123/jomlai.v4i1.5643

Abstract

Skin health is a vital aspect as it functions as the body's primary protector from the external environment. Various skin diseases can arise due to infections, allergies, autoimmune disorders, or environmental factors, and often exhibit similar symptoms, making diagnosis difficult. Artificial intelligence technology, such as Artificial Neural Networks (ANN), offers an innovative solution for accurate diagnosis. One popular ANN method is Backpropagation, which updates network weights iteratively based on the errors produced. This research focuses on applying the Backpropagation algorithm to diagnose skin diseases based on patient symptoms. With a binary data-based system and training using Backpropagation, this system is expected to accurately map symptoms to types of skin diseases. The methodology involves problem identification , data collection (types of skin diseases and symptoms, encoded in binary), dataset and diagnosis rule formation , ANN design (input, hidden, and output layers) , and training and testing using binary data and one-hot encoding. The results indicate that the application of ANN with Backpropagation is effective in assisting the automatic diagnosis process for skin disease cases , achieving an accuracy of 90%. This demonstrates the significant potential of this method in automated medical expert systems.
Analysis of the Impact of Balance Between Work and Study on Student Learning Productivity STIKOM Tunas Bangsa Pematangsiantar Hafizah Rahmi Lubis; Wanda Eka Nugraha; Zaskia Aulia Zahra; Ferdinand Saragih; Victor Asido Elyakim P
JOMLAI: Journal of Machine Learning and Artificial Intelligence Vol. 4 No. 1 (2025): Maret 2025
Publisher : Yayasan Literasi Sains Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55123/jomlai.v4i1.5644

Abstract

Working while studying has become a common phenomenon among students, especially due to the increasing cost of living and education. A balance between study and work is essential to maintain learning productivity, especially for students who are also active in campus organizations. However, many students have difficulty managing their time, which can lead to stress, fatigue, and decreased academic quality. This study highlights the challenges faced by working students, such as high workload, academic demands, and lack of rest time. With good time management, such as creating a clear schedule and utilizing free time effectively, students can achieve a better balance between study and work. Therefore, it is important for students to develop optimal time management strategies to maintain learning productivity and achieve academic success
Application of Backpropagation Algorithm for Prediction of Sales Results of Basic Foodstuffs at Artha Water Store Dwi Safitri Ramadhani; Abdul Ghani Ardiansyah; Damar Arya Prayoga; Riko ILham Nandika; Victor Asido Elyakim P
JOMLAI: Journal of Machine Learning and Artificial Intelligence Vol. 4 No. 1 (2025): Maret 2025
Publisher : Yayasan Literasi Sains Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55123/jomlai.v4i1.5764

Abstract

Companies need to implement sales growth forecasting strategies to create a balance between inventory and sales needs. Without this effort, an imbalance between inventory and sales can cause losses for the company, both in terms of finance and customer satisfaction. As a business entity engaged in the sale of basic necessities, Artha Water is committed to managing its business seriously in order to achieve profits and meet customer needs optimally. However, the development of consumer consumption patterns and sales growth lines at Artha Water which are fluctuating (up and down) make it quite difficult for the cooperative to balance inventory with demand for goods from consumers. By utilizing science in Artificial Neural Networks, we can predict future income using the Backpropagation Algorithm. From the previous description, the author concludes that from the results of the study with the best architecture experiments, namely 12-10-1 to predict sales growth at the Artha Water Store in 2024, it shows an accuracy result of 92%, MSE training of 0.06031588, that there is a significant difference, in other words, sales growth at the Artha Water Store will increase in 2024. With a total sales result of basic necessities at the Artha Water Store for 2024 of IDR 336,930,000.
Analysis of Egg Production Forecasting by Province in Indonesia Using the ARIMA Algorithm Khaswa Giovani Simanungkalit; Muhammad Fikri Azhari; Muhammad ihsan Raditya; Indra Lesmana Putra; Victor Asido Elyakim P
JOMLAI: Journal of Machine Learning and Artificial Intelligence Vol. 4 No. 1 (2025): Maret 2025
Publisher : Yayasan Literasi Sains Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55123/jomlai.v4i1.5765

Abstract

The production of chicken eggs in various regions of Indonesia shows significant variations over time, making it necessary to apply an appropriate predictive approach to support national food planning and distribution strategies. This study employs the ARIMA (AutoRegressive Integrated Moving Average) method to forecast regional chicken egg production based on secondary data from 2018 to 2024. The research steps include data collection, stationarity testing, model parameter determination, as well as the modeling process and result evaluation. The predictions indicate that total national chicken egg production will experience a significant increase, from 12.5 billion eggs in 2025 to 18.57 billion eggs in 2026. Provinces on the island of Java, such as East Java, Central Java, and West Java, are expected to remain the main production centers. Meanwhile, provinces in eastern Indonesia show less stable prediction results, indicating the need for improved data quality and the application of more adaptive models. Overall, the ARIMA model is considered effective for modeling short-term trends, although it has limitations in handling data with high fluctuations.
Implementation of SVM in Predicting Obesity Risk Based on Lifestyle and Dietary Patterns Adinda Febiola; Fahriya Ardiningrum; Michael Orlando A. Purba; Fernando Siahaan; Victor Asido Elyakim P
JOMLAI: Journal of Machine Learning and Artificial Intelligence Vol. 4 No. 1 (2025): Maret 2025
Publisher : Yayasan Literasi Sains Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55123/jomlai.v4i1.5766

Abstract

Obesity is one of the global health issues that has seen a significant increase in recent decades. This condition is closely related to an unbalanced modern lifestyle, such as lack of physical activity, unhealthy eating patterns, and habits of smoking and alcohol consumption. This study aims to analyze the relationship between lifestyle and obesity risk, as well as to evaluate the effectiveness of the Support Vector Machine (SVM) method in predicting the level of obesity risk. The dataset used was obtained from the Kaggle platform, covering various variables such as age, gender, body mass index (BMI), eating habits, sleep patterns, and physical activity. Preprocessing was carried out through data normalization and encoding of categorical variables to ensure data readiness before being input into the model. The SVM model was trained using various training and testing data split ratios and showed a very high accuracy rate, even reaching 100% in some scenarios. These results demonstrate that SVM can effectively identify patterns in lifestyle data that contribute to obesity. Thus, the application of SVM can be a useful predictive tool for healthcare professionals in designing more accurate and efficient data-driven obesity prevention strategies.
Prediction of Poverty Levels in Indonesia Using the Tsukamoto Fuzzy Logic Method Aklima Laduna Ramadya; Tiara Dwi Lestari Purba; Ega Wahyu Andani; Baginda Faustine Sinaga; Victor Asido Elyakim P
JOMLAI: Journal of Machine Learning and Artificial Intelligence Vol. 4 No. 1 (2025): Maret 2025
Publisher : Yayasan Literasi Sains Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55123/jomlai.v4i1.5955

Abstract

Poverty remains a fundamental issue and a primary focus in Indonesia's development. Conventional analysis often fails to provide an accurate picture due to the complexity of its underlying factors. This study aims to build a prediction model for poverty levels in Indonesia using the Tsukamoto fuzzy logic method, based on macroeconomic data from the Central Statistics Agency (BPS) for the years 2022 to 2024. Input variables include inflation rates, unemployment, and economic growth, with the output being the predicted poverty level in percentage. The fuzzy inference process involves fuzzification, rule base formation, fuzzy logic inference, and defuzzification. Data on the percentage of the poor population from BPS shows a decrease from 9.57% in 2022 to 9.27% in 2024. However, significant regional disparities and economic vulnerabilities persist due to global factors like inflation. Fuzzy logic, especially the Tsukamoto fuzzy method, is an adaptive approach capable of handling uncertainty and linguistic variables, while producing numerical outputs. The research results indicate that the fuzzy Tsukamoto model successfully predicts poverty levels with high accuracy, showing an average difference of less than 0.1% from actual data. This finding suggests that the Tsukamoto fuzzy method can be an effective predictive alternative in addressing socio-economic data uncertainties and supporting the formulation of more targeted policies.
Analysis of Unemployment Rate in Indonesia Using Fuzzy Inference System Tiara Dwi Lestari Purba; Aklima Laduna Ramadya; Ega Wahyu Andani; Baginda Faustine Sinaga; Victor Asido Elyakim P
JOMLAI: Journal of Machine Learning and Artificial Intelligence Vol. 4 No. 1 (2025): Maret 2025
Publisher : Yayasan Literasi Sains Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55123/jomlai.v4i1.5956

Abstract

Unemployment is a complex problem that demands an analytical approach capable of handling data uncertainty. This study utilizes a fuzzy inference system to analyze unemployment rates in Indonesia, based on Central Statistics Agency (BPS) data for the 2023-2025 period. The fuzzy logic method was chosen due to its ability to handle linguistic variables and uncertainty in classifying unemployment levels. Input variables include education level, age group, and geographical area, while the output is a classification of unemployment risk (low, medium, high). The fuzzy inference process involves fuzzification, rule base formation, fuzzy logic inference, and defuzzification. BPS data indicates that the Open Unemployment Rate (TPT) experienced a consistent downward trend from 5.45% in February 2023 to 4.76% in February 2025. Nevertheless, the complexity of unemployment requires a flexible approach that can capture nuances of uncertainty, which conventional methods are unable to address. The research results show that the fuzzy inference system is capable of classifying unemployment levels with an accuracy of 87.3%. The highest unemployment rate is found in the 15-24 age group and among high school/vocational school graduates. This system can serve as a decision-making tool for the government in formulating more targeted employment policies.
Analysis of Open Unemployment Rate Prediction Using Backpropagation Method P.A.M. Zidane R.W.P.P. Zer; Dimas Prayogi; M Arif Y Sinaga; Olivia Diwani Saragih; Victor Asido Elyakim P
JOMLAI: Journal of Machine Learning and Artificial Intelligence Vol. 4 No. 2 (2025): Juni 2025
Publisher : Yayasan Literasi Sains Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55123/jomlai.v4i2.5957

Abstract

The open unemployment rate (TPT) is one of the important indicators in assessing the economic health of a region. This study aims to develop an accurate prediction model for the open unemployment rate using the backpropagation algorithm, as well as to evaluate the factors that influence the prediction. Accurate TPT prediction can help the government and policy makers in designing strategies to alleviate unemployment based on the results of the analysis of the developed model. This study aims to analyze and predict the Open Unemployment Rate (TPT) in various provinces in Indonesia in 2024 to 2026 using an artificial neural network model with the Backpropagation algorithm. Based on the test results, the 3-6-1 architecture model showed a prediction ability with 100% accuracy, while other architectures also gave very good results, with 100% accuracy for the 3-3-1 model and 97.06% for the 3-12-1 model. The TPT prediction results show that the unemployment rate is predicted to continue to increase from year to year, indicating the potential for an increase in the number of unemployed in the future. On the other hand, the accuracy analysis shows that each architecture produces different results, with the 3-6-1 architecture producing a longer time for the testing process, but still providing optimal accuracy. This finding illustrates that choosing the right architecture greatly affects the accuracy and efficiency in predicting TPT, which can be an important basis in formulating policies to eradicate unemployment in Indonesia.

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