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Agents: Journal of Artificial Intelligence and Data Science
Published by Universitas Islam Negeri Alauddin Makassar
ISSN : 27469204     EISSN : 27469190     DOI : https://doi.org/10.24252/jagti.v4i1.74
Core Subject : Science, Engineering,
Computer Science & IT Decision Sciences, Operations Research & Management Electrical & Electronics Engineering
The AGENTS published the original manuscripts from researchers, practitioners, and students in the various topics of Artificial Intelligence and Data Science including but not limited to fuzzy logic, genetic algorithm, evolutionary computation, neural network, hybrid systems, adaptation and learning systems, biologically inspired evolutionary system, system life science, distributed intelligence systems, network systems, human interface, machine learning, and knowledge discovery.
Arjuna Subject : Ilmu Komputer (semua kategori) - Kecerdasan Buatan
Articles 40 Documents
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Tinjauan Sentimen Terhadap Ulasan Aplikasi Peminjaman Online dengan Metode Support Vector Machine (SVM) Ghani, ST. Aminah Dinayati; Nur Salman, Nur Salman; Farhan Wahyuta Kusuma, Farhan Wahyuta Kusuma
AGENTS: Journal of Artificial Intelligence and Data Science Vol 4 No 1 (2024): Vol 4 No 1 (2024): September - Februari
Publisher : Prodi Teknik Informatika Universitas Islam Negeri Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24252/jagti.v4i1.74

Abstract

Online loans, abbreviated as "Pinjol," refer to the practice of lending money online through applications or websites without involving traditional financial institutions such as banks or other traditional creditors. Examples of applications in this field include AkuLaku and Kredivo. These apps operate in the e-commerce and credit provision sectors in Southeast Asia, including Indonesia. Despite their operational strengths, these applications have both advantages and disadvantages, leading to positive and negative reviews on platforms like the Play Store. The research aims to identify positive and negative reviews within these online loan applications that can influence users' decisions when choosing a particular app. SVM classification technique is employed to analyze positive and negative sentiments from these reviews. The accuracy results obtained after sentiment analysis for Kredivo are 81%, while for AkuLaku, it is 75%. A higher accuracy value indicates a better ability of the model to predict sentiments correctly. Visualization of impactful words based on word frequency is presented in the form of a Word Cloud. Therefore, based on the sentiment analysis conducted using the SVM model, the author suggests choosing the Kredivo app when selecting an online loan application, as the analysis indicates that Kredivo has better quality compared to AkuLaku.
Public Sentiment Analysis Towards the Implementation of 2024 Election Preparations Using the Maximum Entropy Method Indra Ismawan, Indra Ismawan; Mustikasari, Mustikasari; Saputra, Wahyuddin
AGENTS: Journal of Artificial Intelligence and Data Science Vol 4 No 1 (2024): Vol 4 No 1 (2024): September - Februari
Publisher : Prodi Teknik Informatika Universitas Islam Negeri Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24252/jagti.v4i1.75

Abstract

The General Election Commission (KPU) and the Election Supervisory Agency (Bawaslu) are tasked with coordinating and overseeing elections throughout the territory of Indonesia. Nevertheless, elections are not without various disputes, including violations of ethical codes and administrative issues. General elections are an important way for the public to exercise their political participation rights. In the era of information technology, the public is increasingly active in expressing their opinions and sentiments through social media such as Twitter. Utilizing data from social media, the maximum entropy method is employed to classify opinions found in tweets based on entropy values. This research also aims to measure the accuracy level of the maximum entropy method in sentiment classification. From the results of this study, using approximately 1,400 data, an accuracy rate of 87.08% was obtained, which is quite good and highly feasible for further development to achieve better results
OPTIMALISASI PREDIKSI KELULUSAN MAHASISWA TEPAT WAKTU MENGGUNAKAN BINNING DAN SYNTHETIC MINORITY OVERSAMPLING TECHNIQUE (SMOTE) Rahman, Faidhul; Mustikasari, Mustikasari
AGENTS: Journal of Artificial Intelligence and Data Science Vol 4 No 1 (2024): Vol 4 No 1 (2024): September - Februari
Publisher : Prodi Teknik Informatika Universitas Islam Negeri Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24252/jagti.v4i1.77

Abstract

On-time student graduation is a situation where a student graduates from their educational program at the time planned or determined by the relevant educational institution. This research aims to optimize predictions of student graduation on time using the Binning method to group variables into discrete categories and Synthetic Minority Oversampling Technique (SMOTE) to overcome class imbalances in the dataset. Data containing several variables was analyzed using the Naïve Bayes, Decision Tree and Random Forest machine learning algorithms. Model evaluation is carried out using metrics such as precision, Recall, accuracy, and F1-score. The results confirm that the combination of Binning and SMOTE has a significant impact on increasing prediction accuracy. It is hoped that the results of this research can contribute to increasing the accuracy of predicting student graduation on time. By optimizing the use of Binning and SMOTE, it is hoped that the prediction model can overcome the problem of data imbalance and provide more accurate information to higher education institutions to take the necessary preventive actions to increase student graduation rates and become a reference for similar research in the future.
SISTEM PENDUKUNG KEPUTUSAN REKOMENDASI PEMBERIAN BANTUAN SOSIAL DENGAN MULTI OBJECTIVE OPTIMIZATION BY RATIO ANALYSIS (MOORA) Ramli, Zulhisham; Kambau, Ridwan Andi; Hariani, Hariani
AGENTS: Journal of Artificial Intelligence and Data Science Vol 4 No 1 (2024): Vol 4 No 1 (2024): September - Februari
Publisher : Prodi Teknik Informatika Universitas Islam Negeri Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24252/jagti.v4i1.78

Abstract

Cash Direct Assistance (BLT) is one of the conditional assistance programs from the government as a form of poverty alleviation program. The selection process of potential recipients of BLT in Lamatti Riaja Village, Sinjai Regency, it is not entirely accurate as it is still done manually, resulting in many recipients not meeting the criteria. Based on this, research is conducted to design a decision support system that will facilitate the automatic checking of data for eligible residents who are entitled to BLT funds for each disbursement. This aims to make the selection process more objective, time-efficient, and minimize potential errors in selecting BLT recipients. In this research, the Multi Objective Optimization By Ratio Analysis (MOORA) method is employed. The calculation process utilizes the MOORA algorithm, and the implementation of the system is in the form of a website using the System Development Life Cycle design method, providing good and accurate results. The testing method used is Black Box testing. This research produces a Decision Support System  website with the implementation of a data management subsystem using MySQL. The simulation results of the BLT recipient data calculation using the MOORA algorithm minimize errors in the selection process for potential BLT recipients.
PREDIKSI FAKTOR RISIKO GANGGUAN TIDUR MENGGUNAKAN PENDEKATAN MACHINE LEARNING LOGISTIC REGRESSION DAN GRADIENT BOOSTING Reza, Reza Fitriansyah; Gumelar, R Tommy; Amrizal, Amrizal
AGENTS: Journal of Artificial Intelligence and Data Science Vol 5 No 2 (2025): March - August
Publisher : Prodi Teknik Informatika Universitas Islam Negeri Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24252/jagti.v5i2.85

Abstract

Sleep disorders are one of the major public health issues with broad implications for quality of life, productivity, and chronic disease risks. This study aims to predict risk factors of sleep disorders using a machine learning approach with survey data from the National Sleep Foundation. The research process involved data Cleaning, transformation, normalization, and splitting into training (80%) and testing (20%) sets. Two algorithms were applied, Logistic Regression and Gradient Boosting, and their performance was evaluated using Accuracy, Precision, Recall, and F1-score metrics. SHAP analysis was also employed to assess variable contributions to model predictions. The results indicate that Gradient Boosting outperformed Logistic Regression, achieving perfect Accuracy and F1-score (1.00), while Logistic Regression only reached 0.70. SHAP analysis revealed that sleep duration and quality are the most influential factors, followed by caffeine consumption and age. Therefore, Gradient Boosting not only provides accurate classification but also comprehensive insights into key determinants of sleep disorders, serving as a foundation for more effective health interventions.
ANALISIS SENTIMEN KOMENTAR PENGGUNA TERHADAP GAME MOBA LOKAPALA DI GOOGLE PLAY STORE MENGGUNAKAN ALGORITMA SUPPORT VECTOR MACHINE Muiz, Rafiul; Ishar, Rahmat Fajri; Febrianto, Andi; Akbar, Muhammad Nur
AGENTS: Journal of Artificial Intelligence and Data Science Vol 4 No 2 (2024): March - August
Publisher : Prodi Teknik Informatika Universitas Islam Negeri Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24252/jagti.v4i2.79

Abstract

In this modern era, games are heavily influenced by technological advancements. The development of increasingly complex and captivating games can be played online by millions of players worldwide. The gaming industry in Indonesia has shown significant progress with the emergence of various games from local developers, one of which is Lokapala, a Multiplayer Online Battle Arena (MOBA) game that highlights the uniqueness of Indonesian culture. However, this game has received various responses from users on Google Play Store. This study aims to analyze user sentiment towards the Lokapala game on Google Play Store using the Support Vector Machine (SVM) algorithm. User review data were collected and pre-processed through stages such as data cleaning, tokenization, stopwords removal, and stemming. Subsequently, features were extracted using the TF-IDF method. The analysis results show that SVM with Radial Basis Function (RBF) kernel successfully classified user sentiment with an accuracy of 90% from a total of 300 reviews analyzed. This process not only helps in understanding overall user perceptions but also identifies specific aspects of the game that receive appreciation or criticism. Thus, game developers can use the results of this analysis to improve quality and user satisfaction, and strengthen the game's competitiveness in markets.
Perancangan Game Flashcard dengan Fitur Time Tracker pada Anak Usia Dini Berbasis Android Salsabilah, Fitriyah; Hasrul H, M.; Nur Akbar, Muhammad
AGENTS: Journal of Artificial Intelligence and Data Science Vol 4 No 2 (2024): March - August
Publisher : Prodi Teknik Informatika Universitas Islam Negeri Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24252/jagti.v4i2.81

Abstract

The presidential candidate election in Indonesia is a hot topic on social media, especially Twitter. This study analyzes public sentiment regarding the 2024 presidential candidate election using the IndoBERT model, which is specifically designed for the Indonesian language, on a dataset of 8,442 tweets. This research follows the CRISP-DM methodology, which includes business understanding, data understanding, data preparation, modeling, evaluation, and deployment. The data was collected through crawling with keywords related to the election, followed by preprocessing and manual labeling before being processed by the model. The results show that IndoBERT achieved an accuracy of 98%, with precision, recall, and F1-score also at 98% at the 10th epoch. Batch size evaluation indicated that a batch size of 4 yielded the best performance. This model is effective in classifying sentiment related to the 2024 presidential candidate election and serves as a useful tool for understanding public opinion.
Penerapan Metode Human Centered Design dalam Perancangan Layanan Psikologi Bagi Mahasiswa Fakultas Sains dan Teknologi Madhan, Rahmat Ramadhan; Akib, Faisal; Wahyuni, Sri
AGENTS: Journal of Artificial Intelligence and Data Science Vol 4 No 2 (2024): March - August
Publisher : Prodi Teknik Informatika Universitas Islam Negeri Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24252/jagti.v4i2.82

Abstract

The Human Centered Design method is a method used to better understand the user's comfort and convenience in using a service. In this research, the case study for applying this method is psychology services for students of the Faculty of Science and Technology. This method is usually used in creating UI/UX designs to help designers get to know users better through the level of comfort and convenience that users want when using the services they create. This research aims to apply and test whether the Human Centered Design method is good or not in designing psychological services. It is hoped that the implementation of the Human Centered Design method will be able to test whether this method is suitable for use in designing psychological services for Faculty of Science and Technology students. It is hoped that the results of this research will become new knowledge regarding the application of the Human Centered Design method in case studies of psychological services for students of the Faculty of Science and Technology and can be applied in other case studies.
Penerapan Large Languange Model dalam Analisis Sentimen pada Pemilihan Calon Presiden KHAERUN, A.ALI AKBAR; Afif, Nur; Mustikasari, Mustikasari
AGENTS: Journal of Artificial Intelligence and Data Science Vol 4 No 2 (2024): March - August
Publisher : Prodi Teknik Informatika Universitas Islam Negeri Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24252/jagti.v4i2.83

Abstract

The presidential candidate election in Indonesia is a hot topic on social media, especially Twitter. This study analyzes public sentiment regarding the 2024 presidential candidate election using the IndoBERT model, which is specifically designed for the Indonesian language, on a dataset of 8,442 tweets. This research follows the CRISP-DM methodology, which includes business understanding, data understanding, data preparation, modeling, evaluation, and deployment. The data was collected through crawling with keywords related to the election, followed by preprocessing and manual labeling before being processed by the model. The results show that IndoBERT achieved an accuracy of 98%, with precision, recall, and F1-score also at 98% at the 10th epoch. Batch size evaluation indicated that a batch size of 4 yielded the best performance. This model is effective in classifying sentiment related to the 2024 presidential candidate election and serves as a useful tool for understanding public opinion.
Penerapan Fuzzy Tsukamoto pada Perancangan Sistem Kontrol dan Monitoring Nutrsi Aquaponic Berbasis Internet Of Things Hidayat, Abd Muqsith; Akib , Faisal; Faisal, Faisal
AGENTS: Journal of Artificial Intelligence and Data Science Vol 4 No 2 (2024): March - August
Publisher : Prodi Teknik Informatika Universitas Islam Negeri Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24252/jagti.v4i2.84

Abstract

Aquaponic is an important aquaculture technique because it is easy to apply, saves water, and allows the integration of plant roots to absorb waste nitrogen from fish waste as nutrients. However, temperature, pH, Total Dissolved Solids (TDS), and water level greatly affect plant growth. This research aims to design a control system to monitor plant nutrition and development in real-time using temperature, pH, TDS, and ultrasonic sensors and apply Tsukamoto Fuzzy model to overcome uncertainty in decision making based on sensor data. This research uses a quantitative approach with a design and development method. Data were collected through direct observation, interviews with aquaponic farmers, and related literature studies. The designed system successfully fulfills the need to control and monitor nutrients in aquaponic systems effectively. The system utilizes an ESP8266 module and various sensors (pH, TDS/PPM, temperature, and water level) to monitor water conditions in real-time and send the data to Firebase, which is then displayed on the application interface. Automatic control allows for quick adjustments to changing environmental conditions, ensuring an optimal environment for plant growth.

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