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All Journal Jurnal VOK@SINDO Journal of Information Systems Engineering and Business Intelligence Fountain of Informatics Journal Format : Jurnal Imiah Teknik Informatika Konvergensi Jurnal Ilmu Komputer dan Desain Komunikasi Visual Jurnal Berdaya Mandiri Indexia Jurnal Dinamika Informatika (JDI) Journal of Information Technology and Cyber Security
Mandita, Fridy
Universitas 17 Agustus 1945 Surabaya
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A Child Growth and Development Evaluation Using Weighted Product Method Januantoro, Ardy; Mandita, Fridy
Journal of Information Technology and Cyber Security Vol. 1 No. 1 (2023): January
Publisher : Department of Information Systems and Technology, Faculty of Intelligent Electrical and Informatics Technology, Universitas 17 Agustus 1945 Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30996/jitcs.7613

Abstract

Child development is one of the factors that must be considered in improving a country's education. The level of maturity of human resources is able to maximize starting from childhood. The guidebook of the Ministry of Education and Culture of the Republic of Indonesia (Kemendikbud RI) in 2018 contained six indicators to assess children's learning ability, namely: 1) Moral, 2) Social, 3) Language, 4) Cognitive, 5) Motor, and 6) Art. This study implements these indicators to evaluate children's growth and development. The evaluation method uses the Weighted Product Method (WPM). WPM provides a ranking of the result of the evaluation. In addition, WPM also has an assessment of Beneficial and non-beneficial as a more relevant assessment between indicators. Data were collected by questionnaire at kindergarten schools with the respondents' age average of 5-6 years. The results will be calculated with indicators criteria weights given. The test results recommended for students between 0.65 to 0.62 are as follows: Mahmud, Diko, Cindy, Denny, and Riko. The kindergarten manager can use these recommendations to increase the student's aptitude.
PEMBUATAN RANCANG BANGUN PENERIMAAN SISWA BARU SEBAGAI SARANA PENINGKATAN LAYANAN PADA TK LIYA CIPUNEGARA SURABAYA Mandita, Fridy; Januantoro, Ardy
Jurnal Berdaya Mandiri Vol. 5 No. 2 (2023): JURNAL BERDAYA MANDIRI (JBM)
Publisher : Universitas PGRI Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31316/jbm.v5i2.4794

Abstract

Education is a key aspect in improving Human Resources (HR). Education starts at an early age so that future generations are ready to face increasingly fierce and highly competitive competition. Liya Kindergarten (TK) is one of the educational institutions that participates in providing educational services and focuses on early childhood education. The age range of Kindergarten students is expected to be able to meet the competency criteria for entering Elementary School (SD). In providing information services to the public regarding Kindergarten Liya, it is often difficult to provide up-to-date information to the public regarding the acceptance of new students. This information is in the form of information on school activities, registration information, curriculum information and information on education costs. From these problems, the creation of a new student admissions design for schools is expected to be a solution to the problem to assist the process of registering new students. The website can provide services quickly and accurately with simple steps in registering new students. The method used in this community service is interviews with related users at the location of the community service, namely Liya Kindergarten for a certain period of time and at the end of the community service there is training on using the website to build new student acceptance for users. With this website, it is hoped that it can improve the quality of services at Kindergarten Liya to be able to provide accurate information for people who need it. In addition, it can be a means of promotion related to Kindergarten Liya and on the other hand it can increase the school's income. Keywords: Kindergarten, new student admissions, service quality, information system
PERBANDINGAN ALGORITMA K-NEAREST NEIGHBOR DAN NAÏVE BAYES DALAM MEMPREDIKSI WAKTU KELULUSAN MAHASISWA Mandita, Fridy
Jurnal Dinamika Informatika Vol. 12 No. 2 (2023): Jurnal Dinamika Informatika Vol.12 No.2
Publisher : Universitas PGRI Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Students are an important aspect for higher education institutions, especially regarding the time of student graduation. Therefore, it is critical to know the prediction of the time length for completing studies. This study proposes creating a prediction system for student graduation rates; hence it could be a preventive measure for students to improve their learning process. This research used machine learning techniques to compare the K-Nearest Neighbor (KNN) and Naïve Bayes algorithms. The experiment aimed to determine the best model, such as the amount of data collection, the number of classification classes, and the handling of imbalanced classes. Based on all experiments, the KNN method achieved higher results than the Naïve Bayes method. Applying the SMOTE oversampling technique significantly increased the difference in evaluation scores (precision, recall, F1 score, and accuracy) between 12% and 41% in the Naïve Bayes and KNN methods. The results of the 4-class prediction model using the KNN method with SMOTE get a precision value of 79%, a recall value of 78%, an F1 score of 78%, and an accuracy of 78%. In comparison, the prediction results for eight classes using the KNN method with SMOTE get precision, recall, F1 Score, and accuracy values of 93%.
Classification of Volcanic Status Events Using Autocorrelation and Support Vector Machine Methods Fridy Mandita; Muhammad Arif Fajriyansah
Journal of Information Technology and Cyber Security Vol. 4 No. 1 (2026): January
Publisher : Department of Information Systems and Technology, Faculty of Intelligent Electrical and Informatics Technology, Universitas 17 Agustus 1945 Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30996/jitcs.133023

Abstract

Volcanic eruption disasters occur frequently in Indonesia due to the high density of active volcanoes, posing persistent risks to surrounding communities and infrastructure. Effective mitigation of these hazards is challenged by limitations in monitoring systems, particularly related to instrumentation coverage and the availability of expert human resources. One critical aspect of volcanic monitoring is the accurate classification of seismic activity, which reflects subsurface volcanic processes and supports timely hazard assessment. This study addresses the challenge of reliably classifying volcanic seismic events by proposing an integrated framework that combines autocorrelation-based signal characterization with Support Vector Machine (SVM)–based multi-class classification, supported by Z-score normalization during data preprocessing. The framework is designed to enhance feature consistency and robustness against noise commonly present in volcanic seismic signals. To evaluate its effectiveness, three SVM kernel functions—linear, polynomial, and radial basis function (RBF)—are systematically assessed under identical experimental conditions. The results demonstrate that the polynomial SVM kernel with a degree of two provides the most reliable classification performance, achieving an accuracy of 0.9605. In addition, the application of Z-score normalization substantially improves model stability and overall performance across all kernel configurations, indicating that feature scaling plays a critical role in SVM-based seismic classification. Performance variations among kernels suggest that non-linear feature representations are better suited to capture the complex characteristics of volcanic seismic signals, while classification errors are primarily influenced by class imbalance in underrepresented event types. These findings indicate that the proposed framework effectively supports automated volcanic seismic signal analysis and has the potential to enhance the reliability of seismic-based volcanic activity monitoring.
Klasifikasi Multi-Label Postur Tubuh Berbasis Mediapipe dengan Perbandingan Model Machine Learning Fridy Mandita; Abdul Azizur Rokhman
FORMAT Vol 15 No 2 (2026)
Publisher : Universitas Mercu Buana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/format.2026.v15.i2.007

Abstract

Penelitian ini mengembangkan sistem klasifikasi multi-label untuk mengenali indikasi Forward Head Posture (FHP), Postural Kyphosis (PK), Rounded Shoulder Posture (RSP), dan Normal Posture (NP) melalui analisis citra postur statis berdiri yang dapat merepresentasikan perubahan postural berkaitan dengan gaya hidup sedentari. Meskipun demikian, pengukuran dilakukan melalui analisis citra postur statis dalam posisi berdiri untuk mengamati keselarasan kepala, bahu, tulang belakang, dan panggul. MediaPipe Pose digunakan untuk mengekstraksi 33 landmark tubuh dari setiap citra. Dataset awal terdiri atas 933 citra yang diperoleh dari dataset publik POLAR dan hasil kurasi mandiri. Setelah pemeriksaan kualitas citra dan kelengkapan landmark, sebanyak 54 citra dikeluarkan sehingga diperoleh 879 citra valid. Data dibagi menggunakan Iterative Stratified Split dengan rasio 80:20 untuk mempertahankan distribusi kombinasi label. Sebanyak 132 atribut mentah hasil ekstraksi landmark dan 54 fitur hasil feature engineering membentuk 186 fitur awal. Seleksi fitur menggunakan Mutual Information pada data latih menghasilkan 60 fitur terpilih. Model yang dibandingkan meliputi Light Gradient Boosting Machine, Support Vector Machine, dan Artificial Neural Network. Hasil pengujian menunjukkan bahwa SVM memberikan performa terbaik dengan F1-score macro sebesar 0,76, F1-score micro sebesar 0,79, dan Hamming Loss sebesar 0,19. FHP menjadi kelas dengan performa tertinggi, sedangkan PK menjadi kelas yang paling sulit dikenali karena keterbatasan landmark dua dimensi pada area vertebra torakal. Hasil penelitian menunjukkan bahwa kombinasi MediaPipe Pose, feature engineering, dan machine learning berpotensi mendukung pengembangan sistem pemantauan indikasi postur secara waktu nyata.
Geological Pattern Classification in Seismic Image Data Using a Convolutional Neural Network Fridy Mandita; Argananda Fasha Sadewa
Journal of Information Technology and Cyber Security Vol. 4 No. 2 (2026): July (In progress)
Publisher : Department of Information Systems and Technology, Faculty of Intelligent Electrical and Informatics Technology, Universitas 17 Agustus 1945 Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30996/jitcs.133831

Abstract

Seismic data interpretation is an important stage in geological exploration for identifying subsurface conditions. However, manual interpretation requires considerable time and depends strongly on expert experience. This study develops a Convolutional Neural Network (CNN) model for multi-class classification of geological interval images extracted from the Penobscot Interpretation Dataset. Seven interpreted horizons were used to divide the seismic sections into eight facies interval classes. The preprocessing stages included geological interval extraction, image resizing to 256 × 481 pixels, intensity normalization, and stratified dataset splitting into training, validation, and testing subsets. Five CNN configurations were evaluated by varying the optimizer, learning rate, hidden-layer activation function, and dropout rate. Each configuration was trained repeatedly using five fixed random seeds to evaluate performance consistency. The selected configuration used the Adam optimizer, a learning rate of 0.001, ReLU activation, and a dropout rate of 0.3. It achieved an average testing accuracy of 99.13% ± 0.17%, a macro F1-score of 0.9912 ± 0.0016, and a weighted F1-score of 0.9913 ± 0.0017. The representative run achieved 99.17% accuracy, with weighted precision, recall, and F1-score values of 0.9919, 0.9917, and 0.9917, respectively. These results indicate that the compact CNN produced high and relatively consistent performance for geological interval classification within the evaluated Penobscot dataset. External validation is still required before the model can be generalized to other seismic fields.
Co-Authors Abdul Azizur Rokhman Agus Hermanto Aidil Primasetya Armin Anton Breva Yunanda Argananda Fasha Sadewa Bramantyo Kusumo Jati Januantoro, Ardy Lintang Ragadanu Arizona Muhammad Arif Fajriyansah Rizal Kurnia Pratama Supangat Supangat