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All Journal BAREKENG: Jurnal Ilmu Matematika dan Terapan Jurnal Pengembangan Rekayasa dan Teknologi Variance : Journal of Statistics and Its Applications Community Development Journal: Jurnal Pengabdian Masyarakat Journal of Intelligent Computing and Health Informatics (JICHI) Parameter: Journal of Statistics Journal Of Human And Education (JAHE) Journal of Learning Improvement and Lesson Study Journal of Data Insights Indonesian Council of Premier Statistical Science Prosiding Seminar Nasional Unimus Data Science Insights
Alwan Fadlurohman
Universitas Muhammadiyah Semarang
Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Health Professions Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mathematics Mechanical Engineering Medicine & Pharmacology Nursing Physics Public Health Social Sciences Transportation Other

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OPTIMALKAN PEMAHAMAN DATA DENGAN DASHBOARD MELALUI PELATIHAN VISUALISASI DATA UNTUK SISWA SMA N 1 KEMBANG JEPARA Fadlurohman, Alwan; Fauzi, Fatkhurokhman; Lestari, Febi Anggun; Sarah, Albertus Dion
Community Development Journal : Jurnal Pengabdian Masyarakat Vol. 5 No. 5 (2024): Vol. 5 No. 5 Tahun 2024
Publisher : Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/cdj.v5i5.34893

Abstract

Seiring dengan kemajuan teknologi informasi dan memasuki era revolusi industri 4.0, pemanfaatan teknologi dalam aktivitas manusia semakin meningkat. Data kini menjadi aset berharga, dan kemampuan dalam mengumpulkan, menganalisis, serta menginterpretasi data telah menjadi keterampilan krusial dalam dunia kerja dan pendidikan. Pendidikan memainkan peran penting dalam mempersiapkan generasi mendatang untuk menghadapi tantangan global yang semakin kompleks. Di SMAN 1 Kembang Kabupaten Jepara, terdapat beberapa masalah utama, yaitu keterbatasan akses dan pemahaman siswa mengenai data, kurangnya pengalaman dalam visualisasi data, dan minimnya pemahaman tentang manfaat visualisasi data. Program pelatihan ini bertujuan untuk meningkatkan pemahaman siswa mengenai data dan teknik visualisasi melalui penyampaian materi konseptual tentang konsep data dan visualisasi, penggunaan Google Data Studio, dan pembuatan dashboard visualisasi. Pelaksanaan PKM ini menggunakan metode interaktif dan demontrasi kepada siswa kelas 11 SMAN 1 Kembang sebanyak 20 orang. Hasil PKM menunjukkan bahwa siswa mampu meningkatkan pemahaman mereka tentang konsep dasar data, jenis-jenis data, dan pentingnya visualisasi data. Siswa juga menunjukkan kemajuan yang baik dalam keterampilan teknis terkait penggunaan Google Data Studio untuk mengolah dan memvisualisasikan data. Mereka berhasil menerapkan berbagai fitur untuk menyusun grafik, diagram, dan visualisasi lain yang sesuai dengan data yang diberikan. Hasil visualisasi ini memperlihatkan kemampuan siswa dalam menyajikan data dengan cara yang informatif dan menarik.
Klasifikasi Dataset Diabetes menggunakan Algoritma K-Nearest Neighbors Fitri Diana Musa; M. Al Haris; Dannu Purwanto; Saeful Amri; Alwan Fadlurohman; Ariska Fitriyana Ningrum
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.v2i1.201

Abstract

Data mining merupakan suatu metode yang baik untuk menangani data skala besar. Performasi menjadi penting dalam metode data mining. Salah satu metode yang memiliki performasi terbaik adalah K-Nearest Neighbor (KNN). Artikel ini membahas terkait performasi K-NN. Data yang digunakan pada penelitian ini adalah Diabetes. Data dibagi menjadi 80% data trainingdan 20% data testing. Dengan menggunakan 11 tetangga terdekat, model menghasilkan akurasi sebesar 0.765625. Angka ini mencerminkan kinerja yang baik. Metrik kritis termasuk akurasi sebesar 0.77, presisi sebesar 0.80, dan recall sebesar 0.85. Hasil ini menunjukkan bahwa model KNN memiliki potensi untuk mengklasifikasikan pasien diabetes dengan akurasi yang baik.
Fuzzy Gustafson Kessel for Infrastructure Development Strategy in South Sumatra Province: Fuzzy Gustafson Kessel Untuk Strategi Pembangunan Infrastruktur Di Provinsi Sumatera Selatan Ariska Fitriyana Ningrum; Oktaviana Rahma Dhani; Febi Anggun Lestari; Zahra Aura Hisani; Alwan Fadlurohman
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.v2i2.650

Abstract

Infrastructure development is a strategic element in improving public services and economic growth. South Sumatra Province, with its large economic potential, faces challenges in managing efficient and sustainable infrastructure development. This research aims to apply the Fuzzy Gustafson Kessel (FGK) method in decision making related to infrastructure development in South Sumatra Province. FGK combines fuzzy logic with Gustafson Kessel clustering algorithm to handle uncertainty and data variation from various stakeholders. The data used in this study includes population and geographic census data from the Central Bureau of Statistics of South Sumatra Province in 2023, with five indicators: population, area, population growth rate, population density, and poverty rate. The results show that South Sumatra is divided into three main clusters based on its infrastructure and demographic characteristics. This clustering is expected to improve the effectiveness and efficiency of infrastructure development decision-making, provide more appropriate policy recommendations, and potentially be applied in other regions with similar challenges.
Panel Data Regression Approach to Identify Factors Affecting Unemployment in East Java Province: Pendekatan Regresi Data Panel untuk Mengidentifikasi Faktor-Faktor yang Mempengaruhi Pengangguran di Provinsi Jawa Timur Rizka Amalia Putri; Alwan Fadlurohman; Mardiyah Mughni
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.v3i1.722

Abstract

The Open Unemployment Rate (OOP) in East Java Province is a multidimensional problem influenced by economic and social factors, with significant disparities between districts/cities. This study analyses the effect of Poverty Percentage, Labour Force Participation Rate (TPAK), and Economic Growth on the open unemployment rate using a panel data regression approach to accommodate spatial and temporal heterogeneity. Cross-section (38 districts/cities) and time series (2019-2021) data were analysed through three models: Common Effect Model (CEM), Fixed Effect Model (FEM), and Random Effect Model (REM). The results of statistical tests (Chow, Hausman, and Lagrange Multiplier) show the FEM as the best model with a coefficient of determination of 0.555, explaining 55.5% of the variation in the unemployment rate. The FEM estimation reveals that the Poverty Percentage has a significant positive effect on increasing the unemployment rate, while Economic Growth has a negative impact on reducing the unemployment rate. This finding confirms the need for policies focused on poverty alleviation and increasing economic growth based on regional leading sectors. This study enriches the methodological literature through the application of FEM that controls for region-specific heterogeneity, while providing practical recommendations for policy makers in designing precise unemployment reduction interventions, such as skills training based on industry needs and strengthening labour-intensive programmes.
Geographically Weighted Regression Modeling Using Fixed and Adaptive Kernel Weights for the Human Development Index Case in West Java Province: Pemodelan Regresi Berbobot Geografis Menggunakan Bobot Kernel Tetap dan Adaptif untuk Studi Kasus Indeks Pembangunan Manusia di Provinsi Jawa Barat Karin Karin; Alwan Fadlurohman; Dannu Purwanto
Journal of Data Insights Vol 3 No 2 (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.v3i2.887

Abstract

This study aims to analyze the factors influencing the Human Development Index (HDI) in West Java Province using the Geographically Weighted Regression (GWR) approach. The independent variables used in this study are the Open Unemployment Rate (TPT), School Participation Rate for ages 16–18 (APS_16_18), Population Density, and Gross Regional Domestic Product per Capita (PPK). The modeling was carried out by comparing various kernel functions, namely Gaussian, Bisquare, and Tricube, as well as two bandwidth approaches: fixed and adaptive. The results indicate that the GWR model with a Gaussian kernel and a fixed bandwidth approach provides the best performance based on the lowest AIC value. Compared to the classical Ordinary Least Squares (OLS) model, the GWR model offers a better explanation of spatial variation in HDI across the study area. Although the GWR model was not statistically significant overall based on the ANOVA test, local analysis showed that the variables TPT and PPK had significant effects in all districts and cities, while APS_16_18 and Population Density were not significant in any region. These findings demonstrate that the GWR model is capable of capturing spatial heterogeneity that is not detected by the global regression model.
Comparison of k-Means and Hierarchical Clustering (Ward) Methods for Clustering Regencies/Cities Based on the Food Security Index in West Java Province Al Aghni Naufalia; Nurmawati Ainun Hidayana; Muhammad Bahaudin; Fathir Naufal Hasan; M Al Haris; Alwan Fadlurohman
Data Science Insights Vol. 4 No. 2 (2026): Journal of Data Science Insights
Publisher : PT. Visi Media Network

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63017/jdsi.v4i2.241

Abstract

Food security in West Java Province exhibits significant variation across regencies/cities, influenced by differences in socioeconomic conditions, health, and sanitation. This condition necessitates an analytical approach based on regional clustering to support the formulation of more precise policies. This study aims to cluster regions based on the Food Security Index (FSI) and to evaluate the performance of K-Means and Hierarchical Clustering (Ward) in producing an optimal cluster structure. The data used comprised 27 regencies/cities with eight standardized FSI indicators. Cluster quality was evaluated using the Silhouette Coefficient and the Davies-Bouldin Index. The results indicate that the optimal FSI structure is divided into three clusters, each exhibiting distinct regional characteristics. Both methods produced consistent patterns; however, K-Means demonstrated superior performance to Ward, yielding a Silhouette Coefficient of 0.3048 and a Davies-Bouldin Index of 1.0725, which were respectively higher and lower than those obtained by Ward (0.2806 and 1.1241). This finding indicates the superiority of K-Means in forming more compact and well-separated clusters within relatively homogeneous data. Further analysis reveals that disparities in food security are primarily influenced by access to sanitation, poverty, education, and the ratio of health workers. This study provides empirical evidence on the effectiveness of clustering methods for FSI analysis and offers a regional clustering framework capable of supporting data-driven food security policy formulation at the regional level.
Identifying Regional Welfare Patterns in West Java Based on Socioeconomic Indicators Using DBSCAN Iva Aurellia khalif; Suci Izzati; Siti Wulandari; Radiatul Hidayat; M Al Haris; Alwan Fadlurohman
Indonesian Council of Premier Statistical Science Vol. 5 No. 2 (2026): August 2026
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/icopss.v5i2.39949

Abstract

Regional disparities in welfare remain a significant challenge in Indonesia, particularly in West Java Province, where socioeconomic conditions vary across districts and municipalities. This study aims to classify districts and municipalities in West Java based on socioeconomic characteristics using the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm. The variables analyzed consist of Mean Years of Schooling, Number of Poor Population, Poverty Severity Index, Annual Population Growth Rate, and Human Development Index (HDI). Secondary data were obtained from the West Java Provincial Statistics Office (BPS). Prior to clustering, the variables were standardized using the Z-score method and analyzed using Principal Component Analysis (PCA) to reduce dimensionality and address multicollinearity. The first principal component (PC1), which explained approximately 82% of the total variance, was used as the input for DBSCAN clustering. The optimal DBSCAN configuration was determined by evaluating combinations of epsilon (ε) and minimum number of points (MinPts) using the Silhouette Coefficient. The results showed that ε = 0.06 and MinPts = 2 produced the highest Silhouette Coefficient of 0.8879592, resulting in two clusters and three observations classified as noise. Cluster 1 consisted of 22 districts and municipalities and was characterized by a relatively higher average number of poor population and Poverty Severity Index. Cluster 2, consisting of Sukabumi City and Cimahi City, exhibited higher Mean Years of Schooling and HDI, along with a lower Poverty Severity Index. Pangandaran, Cirebon City, and Banjar City were identified as noise due to their distinctive characteristics. These findings demonstrate that DBSCAN can identify heterogeneous socioeconomic patterns among regions in West Java
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.
Analisis Pengaruh Teknik Preprocessing terhadap Performa CNN pada Klasifikasi Citra Dataset CIFAR-10 Dannu Purwanto; Alwan Fadlurohman
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.v10i1.14651

Abstract

Penelitian ini menganalisis pengaruh teknik preprocessing citra terhadap performa Convolutional Neural Network (CNN) pada dataset CIFAR-10. Teknik yang dievaluasi meliputi normalisasi piksel, augmentasi data, dan filter median yang disusun dalam empat kondisi eksperimen: kontrol, normalisasi, normalisasi+augmentasi, serta normalisasi+augmentasi+median. Setiap kondisi dijalankan sebanyak lima kali menggunakan seed berbeda untuk memperoleh rerata dan standar deviasi. Hasil eksperimen menunjukkan bahwa kondisi kontrol memperoleh akurasi uji tertinggi sebesar 86,42% ± 0,37%, sedangkan normalisasi menghasilkan akurasi yang hampir setara sebesar 86,36% ± 0,28%. Penambahan augmentasi menurunkan akurasi uji menjadi 79,48% ± 2,80%, dan kombinasi normalisasi, augmentasi, serta median filter menghasilkan akurasi terendah sebesar 76,20% ± 2,88%. Temuan ini menunjukkan bahwa preprocessing tambahan yang diuji belum mampu meningkatkan akurasi CNN pada konfigurasi eksperimen ini. Penurunan terutama terjadi pada kelas hewan seperti bird, cat, dog, dan deer, yang mengindikasikan bahwa augmentasi dan median filter dapat mengurangi informasi visual penting pada citra beresolusi rendah.
Co-Authors Al Aghni Naufalia Amalia Annisa, Nafida Amri, Saeful Amrullah, Setiawan Ariska Fitriyana Ningrum Ayu Nur Roosyidah, Nila Dannu Purwanto Dhani, Oktaviana Rahma Endang Tri Wahyuni Maharani Eny Winaryati Fathir Naufal Hasan Fauzi, Fatkhurokhman Febi Anggun Lestari Fitri Diana Musa Gautama, Rahmad Putra Ihsan Fathoni Amri Iksan, Zanaton Haji Indah Manfaati Nur Iva Aurellia Khalif Karin Karin Lestari, Febi Anggun M Al Haris M. Al Haris Mardiyah Mughni Muhammad Bahaudin Nurmawati Ainun Hidayana Oktaviana Rahma Dhani Prasetyawan Aji Sugiharto Prizka Rismawati Arum Radiatul Hidayat Rauf, Rose Amnah Abd Rizka Amalia Putri Rochdi Wasono Rohim, Febrian Hikmah Nur Roosyidah, Nila Ayu Nur Sarah, Albertus Dion Siti Wulandari Suci Izzati Tiani Wahyu Utami Wardani, Amelia Kusuma Yunanita, Novia Yusrin, Yusrin Zahra Aura Hisani