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All Journal PROSIDING SEMINAR NASIONAL Jurnal Statistika Universitas Muhammadiyah Semarang Value Added: Majalah Ekonomi & Bisnis Jurnal Edukasi dan Penelitian Informatika (JEPIN) Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) BAREKENG: Jurnal Ilmu Matematika dan Terapan JTAM (Jurnal Teori dan Aplikasi Matematika) Variance : Journal of Statistics and Its Applications Abdimas UNWAHAS : Jurnal Pengabdian Masyarakat UNWAHAS Community Development Journal: Jurnal Pengabdian Masyarakat Jurnal Litbang Edusaintech Parameter: Journal of Statistics Prosiding Seminar Nasional Official Statistics Journal of Data Insights Prosiding Seminar Nasional Unimus Parameter: Jurnal Matematika, Statistika dan Terapannya Jurnal Meteorologi dan Geofisika LogicLink: Journal of Artificial Intelligence and Multimedia in Informatics Emerging Statistics and Data Science Journal JUSTINDO (Jurnal Sistem dan Teknologi Informasi Indonesia)
Fauzi, Fatkhurokhman
Program Studi Statistika, Universitas Muhammadiyah Semarang
Agriculture, Biological Sciences & Forestry Humanities Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Energy Engineering Health Professions Industrial & Manufacturing Engineering Mathematics Mechanical Engineering Medicine & Pharmacology Nursing Physics Public Health Social Sciences Transportation

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CLUSTERING MODEL K-MEANS PADA KASUS ANGKA PUTUS SEKOLAH TINGKATAN SEKOLAH DASAR DI PROVINSI JAWA TENGAH Laila Khoirun Nisa; Tari Fitri Ningsih; Burhanuddin Izzul Salam; Fauzi, Fatkhurokhman; Eny Winaryati
LogicLink Vol. 1 No. 1, June 2024
Publisher : Universitas Islam Negeri K.H. Abdurrahman Wahid Pekalongan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28918/logiclink.v1i1.7793

Abstract

Basic education aims to equip children with the basic skills they need to navigate their lives as individuals, elements of society, elements of citizens and also as human beings, and prepare them for higher education in the future. The case of dropping out of school seems to be a problem that cannot be overcome. The impact of dropping out of school if not managed properly will certainly be detrimental, including having an impact on the quality of resources in the future. Therefore, it is necessary to take action to reduce the dropout rate at the elementary school level. This research was conducted using the K-Means clustering algorithm to find out which districts/cities in Central Java have high, medium, and low dropout rates. The results of clustering using the K-Means algorithm through 3 methods obtained an optimal K value of 3, therefore 3 clusters were formed from all 35 regencies/cities in Central Java, cluster 1 with a tendency for high elementary school dropout rates to be 14 regencies/cities, cluster 2 with a moderate trend of dropout rates from elementary school there are 15 regencies/cities, and cluster 3 with a low trend of dropout rates from elementary school there are 6 regencies/cities.
Perbandingan Hasil Klasifikasi Data Iris menggunakan Algoritma K-Nearest Neighbor dan Random Forest : Comparison of Iris Data Classification Results using the K-Nearest Neighbor and Random Forest Algorithms Rahman , Budiono; Fauzi, Fatkhurokhman; Amri, Saeful
Journal of Data Insights Vol 1 No 1 (2023): 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.v1i1.135

Abstract

Data mining merupakan suatau metode yang baik untuk menangani data skala besar. Performasi menjadi penting dalam metode data mining. Dua metode yang memiliki performasi terbaik diantaranya K-Nearest Neighbor (KNN) dan Random Forest (RF). Artikel ini membahas terkait perbandingan performasi K-NN dan RF. Data yang digunakan pada penelitian ini adalah Iris. Data dibagi menjadi 80% data training dan 20% data testing. Validasi performasi menggunakan nilai akurasi dan F1-Score. Berdasarkan nilai. Berdasarkan hasil yang didapat metode RF lebih baik dibandingkan dengan metode K-NN. Nilai akurasi yang didapat oleh metode RF adalah 1.00 atau 100% dan nilai F1-Score sebesar 1.00.
Clustering Untuk Menentukan Indeks Kesejahteraan Rakyat di Provinsi Jawa Tengah 2022 Menggunakan Metode Fuzzy C-Means: Clustering to Determine the People's Welfare Index in Central Java Province 2022 Using the Fuzzy C-Means Method Indra Firmansyah; Salmaa Fauziah; Hanif Nur Ibrahim; Fatkhurokhman Fauzi; Tiani Wahyu Utami
Journal of Data Insights Vol 1 No 2 (2023): 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.v1i2.149

Abstract

Kesejahtraan rakyat merupakan salah satu tujuan negara yang tercantum pada Undang-undang Dasar 1945. Dalam meningkatakan kesejahtraan rakyat, tentunya perlu adanya pembangunan yang merata. Untuk menjalankan program pembangunan yang merata, harus dilakukan identifikasi berdsarkan karaktaeristik tingkat kesejahtraan rakyat berdasarkan variabel-variabel yang telah ditentukan agar dalam membuat strategi dan mengambil kebijakan untuk meningkatkan kesejahtraan rakyat dapat tepat sasaran dan optimal. Tujuan dari penelitian ini adalah untuk mengetahui pengelompokkan 35 Kabupatan/Kota di Provinsi Jawa Tengah dan k arakteristik dari setiap kelompok berdasarkan indeks kesejahtraan rakyat. Berdasarkan hasil analisis yang dilakukan, bahwa terdapat 35 Kabupaten/Kota di Provinsi Jawa Tengah dapat membentuk 4 kelompok (cluster), dimana pada cluster 0 beranggotakan 8 Kabupaten/Kota dengan karakteristik Jumlah peduduk miskin tinggi, Daya beli cenderung rendah, rata-rata lama sekolah rendah, angka harapan hidup sangat rendah. Pada cluster 1 terdapat 12 Kabupaten/Kota dengan karakteristik Nilai PDRB sangat tinggi, angka pengangguran relatif rendah, angka lama sekolah relatif tinggi. Pada cluster 2 terdapat 5 Kabupaten/Kota dengan karakteristik Nilai PDRB sangat rendah, jumlah penduduk miskin sangat rendah, daya beli sangat tinggi, kepemilikan rumah rendah, kepadatan penduduk tinggi, daya beli tinggi, angka melek huruf tinggi, rata-rata lama sekolah tinggi, dan yang terakhir cluster 3 terdapat 10 Kabupaten/Kota dengan karakteristik PDRB rendah, kepadatan penduduk sangat rendah, kepemilikan rumah sangat tinggi, daya beli cenderung rendah.
Decision Tree Classification Prediction of Covid-19 Cases in Indonesia: Prediksi Kasus Covid-19 di Indonesia Menggunakan Metode Klasifikasi Decision Tree Arafat, Amaliah Sholeha; Anawai Basman, Aprilla; Fauzi, Fatkhurokhman; Amri, Saeful
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 Putri, Agata Dwi Putri; M. Al Haris; Fauzi, Fatkhurokhman; Amri, Saeful
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.
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.
Klasterisasi Indikator Kesehatan Ibu dan Anak di Indonesia Menggunakan Hierarchical Clustering Agglomerative Angelina, Lea; Putri, Dinda Meyda; Ana, Nisfatun Nurul; Syafira, Elsa Izza; Chumairoh, Kamilah Citra; Syaharani, Nabbila Dyah; Fauzi, Fatkhurokhman
Seminar Nasional Official Statistics Vol 2025 No 1 (2025): Seminar Nasional Official Statistics 2025
Publisher : Politeknik Statistika STIS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34123/semnasoffstat.v2025i1.2401

Abstract

Maternal and child health is a top priority in national development, given that high maternal and infant mortality rates remain a significant challenge in Indonesia. Disparities in health indicators between regions indicate that existing inequalities remain insufficiently addressed. This study aims to group 38 provinces in Indonesia based on 14 maternal and child health indicators for 2024 to identify patterns of disparity. The method used is Agglomerative Hierarchical Clustering with multicollinearity tests (VIF) and KMO for data validation. The complete linkage method was selected for its optimal performance, yielding an agglomerative coefficient of 0.742 and the highest silhouette value of 0.2099 at K = 6. The results formed six clusters reflecting similarities in regional characteristics. Several provinces in Papua clustered separately due to their low health indicator achievements. These findings emphasize the need for region-specific intervention policies to address disparities and promote equitable improvements in maternal and child health.
The Severe Stunting Cases of Children in Central Java Province Explained by Negative Binomial Regression Model Widiyanto, Rhendy K. P.; Fauzi, Fatkhurokhman; Fauzan, Achmad; Kurnia, Anang
JTAM (Jurnal Teori dan Aplikasi Matematika) Vol 10, No 1 (2026): January
Publisher : Universitas Muhammadiyah Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31764/jtam.v10i1.34846

Abstract

Severe stunting, or very short stature among children, remains a critical public health concern in Central Java Province. Robust statistical modelling is essential to identify the key factors associated with these cases and to guide targeted interventions. This study employs count regression models with an offset variable to analyze the factors influencing severe stunting cases across districts in Central Java. By using 2023 official data in districts level taken from the Ministry of Home Affairs and the Statistics Indonesia, we initially utilize a Poisson regression model in this study. However, due to evidence of overdispersion, a Negative Binomial regression model was adopted. Backward elimination was then applied to obtain the most parsimonious model. The Negative Binomial regression successfully addressed overdispersion. Five factors were identified as having a statistically significant influence on severe stunting cases: (1) the proportion of pregnant mothers with Chronic Energy Deficiency receiving nutritious food supplements, (2) the percentage of toddlers (6-23 months) receiving complementary nutritious food, (3) the proportion of households with access to good sanitation, (4) Gross Domestic Product per capita, and (5) the number of local healthcare facilities. These factors have negative relation to the stunting rates, meaning improving these factors will reduce the rates of severe stunting. The findings provide a validated statistical model for severe stunting and offer clear policy directions. To mitigate severe stunting, local governments should prioritize: enhancing nutritious food support for pregnant mothers and toddlers, improving household sanitation, stimulating local economic growth, and increasing accessibility to healthcare facilities.
Co-Authors - Tarsan Achmad Fauzan Achmad Fauzan, Achmad Agung Subakti Nuzulullail Ahmad Amrullah B Alfidha Rahmah Alifia Puspita Sari Alwan Fadlurohman Amri, Saeful Amrullah, Setiawan Ana, Nisfatun Nurul Anang Kurnia Anawai Basman, Aprilla Angelina, Lea Anggun Erya Santika Anisa Ma'u Luthfi Anita Retno Indriani Arafat, Amaliah Sholeha Ardelita Ika Fadhlillah Ardiansyah, Muhammad Rifqy Aulia, Syifa Azizah, Apipah Nur Bagus Sartono Barlian, Seftia Amelia Rizki Burhanuddin Izzul Salam Chumairoh, Kamilah Citra Dannu Purwanto Dewi Ratnasari Wijaya Dwi Agustina Eko Yuliyanto, Eko Eny Winaryati Erika Siva Aulia Erlinda, Relly Fabiola, Gwenda Fadillah, Muhammad Reza Fatikha Adha Fahreza Fauziah Rahma Gabriella Hilary Wenur Hanif Nur Ibrahim Haris, M Al Haris, M. Al Hasbi Assidiqi Iis Widya Harmoko Iis Widya Harmoko Iis Widya Harmoko, Iis Widya Indah Manfaati Nur Indah Manfaati Nur Indra Firmansyah Iqbal Kharisudin Izzah, Nasyiatul Junaidi, Muhammad Rifki Khikman, Muhammad Alvaro Laila Khoirun Nisa Lestari, Febi Anggun Lia Aryanti Sholekhah Litasya Shofwatillah M. Al Haris Moh Yamin Darsyah Moh. Yamin Darsyah Multiyaningrum, Riska Nida Faoziatun Khusna Ninu, Maria Febronia Nugrahanto, Rifqi Nur, Rachmat Kahfiwan Oktavia Sri Banowati Pandiriyan, Muhammad Tegar Permatasari, Shella Heidy Prizka Rismawati Arum Putra, Septian Malik Putri Ayu Firnanda Putri, Agata Dwi Putri Putri, Dinda Meyda Putri, Melfia Verahma Qonita Syalsabilla Handayani Rahma Nurmalita Rahmah, Alfidha Rahman , Budiono Rahman, Budiono Rahmawati, Gita Ramadhan, Abimanyu Arya Rhendy K P Widiyanto Rizma Novinda Puteri Rochdi Wasono RR. Ella Evrita Hestiandari Safril Ahmadi Sanmas Salmaa Fauziah Sam'an, Muhammad Sanmas, Safril Ahmadi Sarah, Albertus Dion Septi Winda Utami Setiayani, Wiwik Shinta Amaria Soffi Amalia Nur Kholifah Syafira, Elsa Izza Syaharani, Nabbila Dyah Syahrani, Nabbila Dyah Syaifullah, Ahmad Reyhan Syifa Aulia Tari Fitri Ningsih Tiani Wahu Utami Tiani Wahyu Utami Watur, Annisa Cahyaningrum Widiyanti, Karin Dita Widiyanto, Rhendy K. P. Wiwit Putri Nur Izzaturrohmah Wulan Sari, Wulan Yan Nazala Bisoumi Yuliardi, Fahrul Raditiar Yuni Nurkuntari