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All Journal Jurnal Statistika Universitas Muhammadiyah Semarang Jurnal Transformatika Pendas : Jurnah Ilmiah Pendidikan Dasar JTAM (Jurnal Teori dan Aplikasi Matematika) Jurnal Pengembangan Rekayasa dan Teknologi Jurnal Ilmiah Pendidikan dan Pembelajaran Community Development Journal: Jurnal Pengabdian Masyarakat Information Science and Library Journal Of Human And Education (JAHE) Journal of Data Insights Journal of Computing Theories and Applications Jurnal Statistika dan Sains Data Journal of Islamic Science Community Journal of Learning on History and Social Sciences Journal of Computing and Smart Ecosystems
Amri, Saeful
Universitas Muhammadiyah Semarang
Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Health Professions Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mathematics Mechanical Engineering Public Health Social Sciences Transportation Other

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DASHBOARD LINGKUNGAN HIDUP UNTUK ANALISIS DIARE MENGGUNAKAN METODE K-MEANS CLUSTERING Sitti Sahara; Saeful Amri; Ariska Fitriyana Ningrum; Dannu Purwanto
Journal of Data Insights Vol 2 No 1 (2024): Journal of Data Insights
Publisher : Department of Sains Data UNIMUS Universitas Muhammadiyah Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26714/jodi.v2i1.210

Abstract

Abstrak Singkat: Diare adalah penyakit umum dengan penyebab yang beragam, termasuk virus, bakteri, dan faktor-faktor lainnya. Faktor-faktor lingkungan, gizi yang buruk, dan kurangnya pengetahuan masyarakat berperan penting dalam tingginya kasus diare, terutama pada anak-anak di bawah lima tahun, di Indonesia. Analisis cluster digunakan untuk mengelompokkan daerah berdasarkan kasus diare dan membantu perencanaan penanggulangan. Penelitian ini menggunakan data BPS 2021 dari 34 provinsi di Indonesia dan berfokus pada faktor penyebab diare. Penelitian ini bertujuan untuk memahami faktor-faktor yang berkontribusi pada kasus diare, dengan harapan dapat merumuskan strategi penanggulangan yang lebih efektif.
Prediction of Covid-19 Cases in Indonesia Using the Auto Regressive Integrated Moving Average Method: Prediksi Kasus Covid-19 di Indonesia Menggunakan Metode ARIMA Asriyanti Sawiah Adam; Rahma Safira; M. Al Haris; Saeful Amri
Journal of Data Insights Vol 3 No 1 (2025): Journal of Data Insights
Publisher : Department of Sains Data UNIMUS Universitas Muhammadiyah Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26714/jodi.v3i1.212

Abstract

This study discusses the use of the ARIMA (Auto Regressive Integrated Moving Average) model to predict the number of COVID-19 cases in Indonesia based on previous data. The results of the analysis show that the ARIMA (1,0,0) model is the most accurate in predicting the spread of COVID-19. Based on this model, the prediction results obtained that confirmed COVID-19 data from January to December 2022 are predicted to decrease. The number of confirmed cases of COVID-19 until December 2022 is predicted to reach 20,0365 cases of spread. So this Covid-19 case still needs special and more serious attention from the government and the public must still be vigilant because based on the results of the study there have been no signs of a significant decrease in the spread of Covid-19 cases. This study provides important insights for the government, medical personnel, and the public in planning strategies for preventing and handling the pandemic
Decision Tree Classification Prediction of Covid-19 Cases in Indonesia: Prediksi Kasus Covid-19 di Indonesia Menggunakan Metode Klasifikasi Decision Tree Amaliah Sholeha Arafat; Aprilla Anawai Basman; Fatkhurokhman Fauzi; Saeful Amri
Journal of Data Insights Vol 2 No 2 (2024): Journal of Data Insights
Publisher : Department of Sains Data UNIMUS Universitas Muhammadiyah Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26714/jodi.v2i2.213

Abstract

Forecasting is the prediction of an event in the present and future using past event data. The purpose of forecasting is to minimize errors in predictions (forecast errors) to provide a higher level of confidence. In the context of the COVID-19 pandemic, forecasting the number of cases can help anticipate surges, allowing for better-preparedness to minimize its impact. Forecasting methods can be categorized into three common classifications: qualitative methods, time series, and causal methods. Time series methods are further divided into statistical methods and machine learning. Machine learning methods are more effective in forecasting as they can accommodate non-linear and complex relationships between inputs and outputs. One of the machine learning methods used is the Decision Tree, which is a predictive model structured in a tree or hierarchical format. The Decision tree is a data processing method for predicting the future by constructing classification and regression models in a tree structure. The decision tree is also the most popular and easily understood classification method. In this study, a classification decision tree is used to forecast positive COVID-19 cases in Indonesia using the Python programming language.
K-Nearest Neighbor (KNN) Method for Weather Data Prediction: Penerapan Metode K-Nearest Neighbour (KNN) Untuk Prediksi Data Cuaca Agata Dwi Putri Putri; M. Al Haris; Fatkhurokhman Fauzi; Saeful Amri
Journal of Data Insights Vol 3 No 1 (2025): Journal of Data Insights
Publisher : Department of Sains Data UNIMUS Universitas Muhammadiyah Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26714/jodi.v3i1.214

Abstract

The weather tends to change frequently every day, so weather forecasts are made to be used as an early warning if sudden weather changes occur. By forecasting the weather, losses can be minimized and people are alert to carry out outdoor activities. From this problem, the K-Nearest Neighbor (KNN) method was applied. This method is expected to provide accurate and efficient information to obtain weather predictions for existing conditions. The data used is secondary data. After conducting research on training data (old data) amounting to 80% and test data (new data) amounting to 20%. The accuracy results from the testing data predictions are 75% with a value of k = 8.
K-Nearest Neighbor Algorithm in Classification of Stunting Detection Dataset: Algoritma K-Nearest Neighbor dalam Klasifikasi Dataset Deteksi Stunting Lea Angelina; Saeful Amri; M Al Haris; Rochdi Wasono; Erna Julia Nanga; Faninda Aidina Fitri
Journal of Data Insights Vol 4 No 1 (2026): Journal of Data Insights
Publisher : Department of Sains Data UNIMUS Universitas Muhammadiyah Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26714/jodi.v4i1.752

Abstract

Stunting is a nutritional problem that can affect children's physical growth and cognitive development and has a long-term impact on the quality of future generations. Early detection of stunting is crucial to enable timely and effective interventions. As technology advances, machine learning algorithms such as K-Nearest Neighbors (KNN) offer potential solutions to improve the accuracy of stunting risk classification. This study aims to design a classification model based on the K-Nearest Neighbors (KNN) algorithm in the early detection of stunting risk in toddlers. This research uses the 2024 stunting dataset obtained from Kaggle. The data is analyzed through the stages of cleaning, transformation, and division into training and testing data. The KNN model was tested with various K values to determine the optimal value. The results showed that the KNN model with a value of K=8 resulted in an accuracy of 93.80%, F1-Score of 93.65%, precision of 93.63%, and recall of 93.79%. This shows that KNN is reliable in classifying the nutritional status of toddlers and can be applied in stunting prevention efforts using more accurate data. This research contributes to developing machine learning-based classification systems that can support decision-making in public health programs, especially in reducing stunting rates.
ADASYN-Based Multiclass Support Vector Machine for Village Development Index Classification in North Maluku Province: Support Vector Machine Multikelas Berbasis ADASYN untuk Klasifikasi Indeks Pembangunan Desa di Provinsi Maluku Utara Tiani Wahyu Utami; Lea Angelina; Saeful Amri
Journal of Data Insights Vol 4 No 1 (2026): Journal of Data Insights
Publisher : Department of Sains Data UNIMUS Universitas Muhammadiyah Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26714/jodi.v4i1.1154

Abstract

Class imbalance is a significant constraint that can diminish the performance of classification models. This study implements the integration of Adaptive Synthetic Sampling (ADASYN) and Multiclass Support Vector Machine (SVM) to classify the 2024 Village Development Index (IDM) in North Maluku Province. The dataset comprises 684 villages, utilizing the Social Resilience Index (IKS), Economic Resilience Index (IKE), and Environmental Resilience Index (IKL) as predictor variables. The data was partitioned using a ratio of 80% for training and 20% for testing. An extreme imbalance was identified in the "independent village" category (0.88%); therefore, ADASYN was applied to the training data to generate 862 synthetic samples to balance the class distribution. The optimal model yielded by the process was a linear kernel SVM with a Cost value of 100, yielding an accuracy of 98.54%, precision of 98.26%, recall of 99.4%, and an F1-score of 98.83%. Of the total 137 villages evaluated, only two villages were misclassified: Salimuli Village and Dowongimaiti Village. These findings demonstrate the effectiveness of the ADASYN-SVM combination in producing accurate classifications to support village development policies in island regions.
MANAJEMEN PEMBIAYAAN PENDIDIKAN BERBASIS MASYARAKAT DALAM PENINGKATAN KUALITAS PENDIDIKAN Amri, Saeful; Martini, Tri; Sutrisno
Pendas : Jurnal Ilmiah Pendidikan Dasar Vol. 11 No. 02 (2026): Volume 11 No. 2, Juni 2026 Publish
Publisher : Program Studi Pendidikan Guru Sekolah Dasar FKIP Universitas Pasundan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23969/jp.v11i02.50597

Abstract

This study aims to examine community-based education financing management in improving the quality of education using the Systematic Literature Review (SLR). The research data sources consist of scientific journal articles published between 2021 and 2025 and obtained through the Google Scholar, Scopus, Elsevier, and other relevant scientific journal databases. Article selection was based on inclusion and exclusion criteria covering topic relevance, research article type, open access, and journal quality. Data analysis was conducted through the stages of data collection, data reduction, data presentation, and drawing conclusions based on the Miles,Huberman, and Saldana model, using content analysis techniques to identify relationships between concepts and patterns of association in the study. The results of the study indicate that community-based education financing management is carried out through financial planning, building partnership networks, business development, strengthening social capital, implementing fund management, financial reporting transparency, and evaluating the use of funds. Community participation in the form of financial support, cooperation, oversight, and involvement in decision-making has a positive impact on the sustainability of learning, the development of infrastructure, the welfare of teachers, and the overall improvement of education quality.
Analisis Peramalan Suhu Permukaan Bumi di Kota Semarang Menggunakan Regresi Nonparametrik dengan Estimator Deret Fourier Berdasarkan Penalized Least Square (PLS) Ihsan Fathoni Amri; Tiani Wahyu Utami; Dannu Purwanto; Alwan Fadlurohman; Ariska Fitriyana Ningrum; Saeful Amri
Jurnal Pengembangan Rekayasa dan Teknologi Vol. 10 No. 1 (2026): Mei (2026)
Publisher : Universitas Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26623/jprt.v10i1.14583

Abstract

Perubahan iklim global yang ditandai oleh peningkatan suhu permukaan menjadi isu penting, terutama di wilayah perkotaan dengan tingkat urbanisasi tinggi seperti Kota Semarang. Peningkatan suhu dapat memengaruhi kualitas lingkungan dan kenyamanan masyarakat, sehingga diperlukan pemodelan dan peramalan yang akurat untuk memahami pola perubahannya. Penelitian ini bertujuan membentuk model regresi nonparametrik menggunakan estimator deret Fourier dengan optimasi Penalized Least Square (PLS) serta meramalkan suhu permukaan di Kota Semarang. Parameter optimal ditentukan berdasarkan nilai Generalized Cross Validation (GCV) minimum. Hasil penelitian menunjukkan bahwa model terbaik diperoleh pada koefisien Fourier  dengan lambda optimal 0,00027 dan GCV minimum 0,81182. Model menghasilkan Mean Absolute Percentage Error (MAPE) sebesar 1,203717% dengan akurasi 98,7963%, yang termasuk kategori sangat baik. Hasil ini menunjukkan bahwa pendekatan deret Fourier berbasis PLS efektif dalam memodelkan dan meramalkan suhu permukaan di Kota Semarang.
Co-Authors Abdul Karim Agata Dwi Putri Putri AHMADI Ali Imron Alwan Fadlurohman Amaliah Sholeha Arafat Anggry Windasari April Firman Daru Aprilla Anawai Basman Ariska Fitriyana Ningrum Asriyanti Sawiah Adam Basirudin Ansor Budiono Rahman Dannu Purwanto Dhani, Oktaviana Rahma Edy Winarno Eka Suryani, Puput Erna Julia Nanga Faninda Aidina Fitri Fauzi, Fatkhurokhman Fisabilillah, Muh. Irodat Fitri Diana Musa Gansar Timur Pamungkas Hanif, Mohammad Burhan Haris, M. Al Ihsan Fathoni Amri Indah Fitriyani Indah Manfaati Nur Indah Manfaati Nur ine fitriani Ismi Elya Wirdati Khoirudin Khoirudin Khoirudin Khoirudin, Khoirudin Lea Angelina M. Al Haris Manfaati Nur, Indah Masudah, Nurhidayatul Miftakhiyah Fazza Baita Mohammad Ridwan Muhammad Ivan Ardiansyah Nur, M. Saifudin Panata, Helmi Prasetio Prajanto Wahyu Adi Prasetyo, Noer Prizka Rismawati Arum Purnomo Putro, Dwi Puspitasari, Listi Putro, Dwi Purnomo Rahma Safira Ridwanulhaq, Alfina Fauziah Rifa i, Ahmad Rochdi Wasono Rohani, Imam Siti Nurhalisa Sitti Sahara Supriadin Supriadin Supriyadin, Supriyadin Sutrisno Tiani Wahyu Utami Tri Martini Wardani, Amelia Kusuma Wendy Sarasjati