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All Journal Sistemasi: Jurnal Sistem Informasi JOIV : International Journal on Informatics Visualization
Gunawan, Puguh Hasta
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Computer Science & IT Electrical & Electronics Engineering

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Detection of Graduation Potential in Prospective Students using the Random Forest Algorithm Gunawan, Puguh Hasta; Paputungan, Irving Vitra
Sistemasi: Jurnal Sistem Informasi Vol 14, No 5 (2025): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

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Abstract

Detecting students’ graduation potential is commonly performed by evaluating various academic and non-academic factors. This study aims to develop a predictive model for student graduation from the beginning of their academic journey, utilizing high school academic data such as grades, attendance, study hours, as well as demographic and social factors. The goal is to enable universities to identify students who are at risk of delayed graduation. With accurate predictions, institutions are expected to design more targeted academic interventions, such as tutoring, counseling, or other forms of academic support. A total of 396 student records were used in this study and processed through a series of preprocessing steps, including the removal of irrelevant data and the encoding of categorical variables. The model was developed using the Random Forest algorithm with parameters set to max_depth = 15 and random_state = 42. Model performance was evaluated using accuracy, recall, F1-score, and the ROC curve. The results show that the model achieved an accuracy of 89%, with the Pass class having a recall of 87% and an F1-score of 91%, and the Fail class showing a recall of 92% and an F1-score of 84%. Additionally, the Area Under the Curve (AUC) value of 0.94 indicates excellent model performance in distinguishing between students likely to graduate and those at risk of not graduating. This study confirms that the model is effective in classifying graduation outcomes based on early academic data. For further development, it is recommended to include additional variables such as psychological factors, learning motivation, and socioeconomic conditions. Moreover, tuning the model by adding other parameters—such as n_estimators, min_samples_split, and max_features—is suggested to improve the model’s accuracy and generalizability.
Optimization and Collaboration of Fuzzy C-Mean, K-Mean, and Naïve Bayes Algorithms Using the Elbow Method for Micro, Small, and Medium Enterprises Norhikmah, -; Nurastuti, Wiji; Aminuddin, Afrig; Sidauruk, Acihmah; Gunawan, Puguh Hasta
JOIV : International Journal on Informatics Visualization Vol 9, No 5 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.5.3292

Abstract

Micro, Small, and Medium Enterprises (SMEs) have a vital role in Indonesia’s economy. However, IT-based marketing strategies among SMEs receive limited support from the government due to the lack of sufficient data to inform policy. This study aims to (1) identify the needs of SMEs for social media promotion training as part of their digital capacity building, (2) develop and compare the effectiveness of classification models that combine Fuzzy C-Means and K-Means clustering algorithms with the Naïve Bayes algorithm to group SMEs based on business characteristics, (3) analyze the relationships between business variables—such as business type, marketing media, funding sources, and financial aspects—and SME performance through regression analysis, and (4) provide data-driven foundations for designing targeted digital interventions and policy strategies to support SME development in Indonesia. This study used UPPKS data from 133 SMEs in seven districts in the Special Region of Yogyakarta. Data analysis covered business types, marketing platforms used, funding sources, and financial performance indicators. Data pre-processing involved cleaning, normalization, and integration to ensure consistency and readiness for analysis. The researcher used the Elbow method to determine the optimal number of clusters. Then, it also used Fuzzy C-Means (FCM) and K-Means to categorize SMEs into three groups: high, medium, and low. The classification was based on the Naïve Bayes algorithm. The evaluation of the model performance used a confusion matrix, cross-validation, and regression analysis to examine inter-variable relationships. The results showed that the combination of FCM and Naïve Bayes achieved an accuracy of 85% based on the confusion matrix and 97% based on cross-validation. Meanwhile, the combination of K-Means and Naïve Bayes respectively achieved an accuracy of 96% and 94.7%. These findings demonstrate the effectiveness of the proposed approaches in classifying SMEs based on their characteristics and performance. This research provides important insights for policymakers and SME development agencies in designing more targeted digital training and support programs. Future studies should explore the integration of other algorithms, such as Support Vector Machines (SVM) and Decision Trees, while incorporating market trends and customer engagement to enhance SME classification and provide ongoing support.
Co-Authors Acihmah Sidauruk Afrig Aminuddin Norhikmah, - Paputungan, Irving Vitra Wiji Nurastuti, Wiji