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All Journal Jurnal Transformatika Variance : Journal of Statistics and Its Applications LOSARI: Jurnal Pengabdian Kepada Masyarakat Journal Of Human And Education (JAHE) Berita Kedokteran Masyarakat Journal of Data Insights Prosiding Seminar Nasional Unimus
Dannu Purwanto
Universitas Muhammadiyah Semarang
Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Earth & Planetary Sciences Economics, Econometrics & Finance Education Engineering Environmental Science Health Professions Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Nursing Public Health Social Sciences Transportation Other

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Rancang Bangun Sistem SIANIDA (Sistem Administrasi Digital Desa) Sebagai Upaya Akselerasi Pelayanan di Desa Sidorejo - Tarsan; Alfidha Rahmah; Ahmad Amrullah B; Qonita Syalsabilla Handayani; Rahma Nurmalita; Soffi Amalia Nur Kholifah; Yan Nazala Bisoumi; Dannu Purwanto; Fatkhurokhman Fauzi
Prosiding Seminar Nasional Unimus Vol 6 (2023): Membangun Tatanan Sosial di Era Revolusi Industri 4.0 dalam Menunjang Pencapaian Susta
Publisher : Universitas Muhammadiyah Semarang

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Abstract

Desa Sidorejo di Kecamatan Karangawen, Kabupaten Demak, Provinsi Jawa Tengah, memiliki tantangansignifikan dalam pengelolaan administrasi desanya. Adanya populasi 7.000 jiwa dan hanya 18 perangkatdesa, mengakibatkan rasio antara perangkat desa dan penduduk tidak seimbang (1:388), sehinggamempengaruhi efisiensi dan pelayanan administratif. Solusi untuk mengatasi permasalahan tersebut, timpengabdian masyarakat yang terdiri dari Himpunan Mahasiswa Statistika dan dosen mengusulkan solusimelalui implementasi “Sistem Administrasi Digital Desa (SIANIDA)”. SIANIDA merupakan website desauntuk pengajuan surat online yang dapat mengurangi antrian, dan meningkatkan efisiensi. Implementasisistem tersebut dilakukan dengan sosialisasi dan pelatihan bagi perangkat desa dan masyarakat. Pelatihandilakukan secara bertahap untuk masing-masing RT, RW, dan perangkat desa. Hasil yang diperoleh adalahsistem dapat meningkatkan kecepatan pelayanan administrasi desa. Pengembangan selanjutnya adalahmemasifkan penggunaan sistem SIANIDA.Kata Kunci : Pelayanan administratif, sistem administrasi digital, sistem informasi
GLOBAL THRESHOLDING IMPLEMENTATION FOR NOISE HANDLING IN DIGITAL IMAGE RECOGNITION Purwanto, Dannu; Agustiyar, Agustiyar
Jurnal Transformatika Vol. 21 No. 2 (2024): Januari 2024
Publisher : Jurusan Teknologi Informasi Universitas Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26623/transformatika.v21i2.8713

Abstract

Text recognition (OCR - Optical Character Recognition) is a research field that is gaining widespread attention due to its wide application in image and document processing. Although OCR technology has achieved a high level of success, the main challenge faced is the presence of noise in text image, noise causes decreased text recognition results, noise causes miss classification. Therefore needed noise handling text recognition.  The aim of this research is to provide valuable insight into the techniques and approaches used in the context of noise treatment using global threshold methods. The method used starts from an input digital image, then preprocessing is carried out by converting the image into a gray scale image, then a threshold is applied to the image, then recognition is carried out. From 6 experiments, the best results were obtained for character recognition with a threshold value (t) of 65 and a character recognition accuracy percentage of 94.29%. T value determined manually and static for separates the all object and the background, while in reality the lighting or contrast always varies. Suggestions for further research include developing an adaptive thresholding method approach to obtain threshold values automatically and optimally. So that if faced with varying lighting conditions or contrast, better results can be obtained.
Implementasi Teknologi Ramah Lingkungan untuk Menunjang Sektor Pertanian di Desa Margohayu Karangawen Demak M. Al Haris; Dannu Purwanto; Ali Imron; RA. Qonita Syalsabilla Handayani; Arya Praditya
LOSARI: Jurnal Pengabdian Kepada Masyarakat Vol. 6 No. 2 (2024): Desember 2024
Publisher : LOSARI DIGITAL

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53860/losari.v6i2.349

Abstract

Margohayu village is one of the regions in the Karangawen Subdistrict, Demak Regency, Central Java. Desa Margohayu has a population of 8,056, with the majority working as farmers. Currently, the irrigation of rice fields in Desa Margohayu still relies on fossil fuel-based energy, which gradually depletes and has environmental consequences. However, Desa Margohayu has significant potential to establish a self-sustaining energy system by harnessing solar energy. Sunlight can be converted into electricity through solar panels to power water pumps. Therefore, the Community Service Team from Universitas Muhammadiyah Semarang proposes an environmentally friendly and sustainable technology implementation in the agricultural sector through the Margo Mulyo Farmer Group in Desa Margohayu. The goal is to reduce the negative impact of fossil fuel usage that has been prevalent. The results of this initiative show that the Margo Mulyo Farmer Group gains knowledge and skills related to solar energy utilization. The implementation of this technology is expected to reduce agricultural costs, particularly in the irrigation process.
Data Visualization Excellence: Google Data Studio Workshop At Sekolah Indonesia Kuala Lumpur Amri, Saeful; Fadlurohman , Alwan; Ningrum, Ariska Fitriyana; Purwanto, Dannu; Amri , Ihsan Fathoni; Wardani, Amelia Kusuma; Dhani, Oktaviana Rahma
Journal Of Human And Education (JAHE) Vol. 5 No. 1 (2025): Journal of Human And Education (JAHE)
Publisher : Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/jh.v5i1.2178

Abstract

The development of information technology and the entry of the industrial revolution 4.0 era has led to the inseparability of human activities related to the use of technology. In today's rapidly growing information age, data is one of the most valuable assets. The ability to collect, analyze, and interpret data is becoming a very important skill not only in the world of work but also in education. Education is the foundation for preparing future generations for increasingly complex global challenges, and a good understanding of data can provide a significant competitive advantage. In schools, the ability to analyze and interpret data is becoming an invaluable skill for students. Along with the development of technology, data visualization has become an effective method to convey information in a more comprehensible manner. In this context, Google Data Studio offers a powerful and easy-to-use tool for creating interactive dashboards that help in analyzing and presenting data. Indonesian Migrant Workers (TKI) are Indonesian citizens who live and work abroad. TKI provide a large contribution of foreign exchange to the country of Indonesia. However, there are problems in the field of education for children whose parents work as TKI in Malaysia, especially education that is relevant to success in terms of opening their own jobs abroad. This is considered important because to get jobs in government agencies or companies in Malaysia, the children of TKI working in Malaysia must compete with job seekers who are Malaysian citizens. One alternative that can be taken to overcome competition in getting jobs is to create your own jobs. Opening your own jobs is not an easy thing. Knowledge and insight about this are needed which are given early on to the children of TKI in school. By teaching Google Data Studio in the form of data visualization to students, they not only learn how to read and interpret graphs and diagrams, but also how to present their own data in a more interesting and informative way. This ability will be very useful in the future, both in academic and professional environments. By providing insight into Google Data Studio to students in schools, these students have the provisions to be able to read data and have the opportunity to work and get decent jobs. As a Community Service activity with an international scope, this activity takes partners in Malaysia, namely the Indonesian School-Kuala Lumpur - SIKL which is located in Sentul, Kuala Lumpur, Federal Territory of Kuala Lumpur. The Community Service Team of Muhammadiyah University of Semarang is very receptive to criticism and suggestions so that the implementation of Community Service in the future can be even better.
CLUSTERING OF DISTRICTS IN CENTRAL JAVA ACCORDING TO PEOPLE'S WELFARE INDICATORS USING WARD'S METHOD Purwanto, Dannu; Pratama, Rizky Adi; Lein, Raymond Bolly; Prastyo, Ikwan; Haris, M. Al
VARIANCE: Journal of Statistics and Its Applications Vol 7 No 1 (2025): VARIANCE: Journal of Statistics and Its Applications
Publisher : Statistics Study Programme, Department of Mathematics, Faculty of Mathematics and Natural Sciences, University of Pattimura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/variancevol7iss1page73-82

Abstract

One of the main goals of development activities carried out by every country was to improve people's welfare. Community welfare was a situation where citizens could fulfill and adequately fulfill their material and spiritual needs. The poverty rate of Central Java Province was recorded: out of a total population of 37.03 million people, around 3,831.44 thousand people were poor. The population density of Central Java Province reaches 1,120 people per km2, the third largest number of poor people in Indonesia. This study aimed to group regencies/cities in Central Java based on the characteristics of the community welfare indicators. The indicators used in this study were the Open Unemployment Rate (UR), Labor Force Participation Rate (LFPR), Poverty, Human Development Index (HDI), and District Minimum Wage (DMW). The method used in this research was Ward's Agglomerative Hierarchical Clustering. The final results concluded that the best number of clusters formed was 6 clusters. The first cluster consists of 13 Regencies/Cities, the second cluster consists of 8 Regencies/Cities, the third cluster consists of 3 Regencies/Cities, the fourth cluster consists of 1 Regency/City, the fifth cluster consists of 5 Regencies/Cities, the sixth cluster consisting of 5 Regencies/Cities.
Social media engagement patterns in relation to adolescent anxiety and depression: a systematic review Amanda, Qorry; Haryani, Deby Aprilia; Sofa, Yulia Ratna; Purwanto, Dannu; Maryam, Adiva Kalila; Basrowi, Ray Wagiu; Devi, Yuli Puspita
BKM Public Health and Community Medicine Vol 41 No 09 (2025)
Publisher : Universitas Gadjah Mada

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/bkm.v41i09.22664

Abstract

Purpose: Adolescents are increasingly immersed in social media environments that encourage curated self-presentation and social comparison. Global prevalence estimates from the World Health Organization indicate that 5.5% of adolescents aged 15–19 meet diagnostic criteria for anxiety disorders. While prior research emphasized screen time duration, emerging evidence suggests that how young people engage— primarily through passive, emotionally reactive, or appearance-focused behaviors—may be more predictive of internalising symptoms such as anxiety and depression. This systematic review aimed to synthesize observational evidence on associations between adolescent social media use and internalising symptoms, with particular attention to engagement patterns, psychosocial mediators, and contextual moderators. Methods: A comprehensive search across PubMed, Scopus, EBSCOhost, and AI-assisted platforms was conducted between April 14 and 25, 2025. Inclusion criteria were: observational design, adolescent population (10–19 years), validated measures of anxiety/depression, and exposure to social media use. Ten studies published between 2018 and 2025 met eligibility criteria (N=10). Data were synthesized narratively, with quality assessed using the JBI checklist. Results: Across studies, problematic use and passive scrolling were more strongly associated with anxiety and depression than total time spent online. Sleep disruption and appearance-based comparison consistently emerged as mediators, while gender, emotional reactivity, and socioeconomic background moderated vulnerability. Girls and gender- diverse adolescents reported higher psychological reactivity. Protective factors included physical activity and family support. Most studies were cross-sectional; only one referenced neurobiological pathways. Conclusion: Digital mental health risks in adolescents are driven less by screen exposure time and more by emotionally charged engagement styles. Interventions should prioritize resilience-building and digital literacy, while future research must incorporate longitudinal and biopsychosocial frameworks to capture the complexity of these associations better.
Study on Import and Export Indicators in Indonesia Using Volatility and Markov Switching Model Combination: Kajian Indikator Impor dan Ekspor di Indonesia Menggunakan Kombinasi Model Volatilitas dan Markov Switching Fathoni Amri, Ihsan; Purwanto, Dannu
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.v1i1.137

Abstract

Krisis ekonomi tahun 1997 merupakan masalah yang terjadi di hampir semua negara berkembang termasuk Indonesia. Berdasarkan krisis ekonomi, diperlukan indikator performance knowledge. Impor dan ekspor merupakan indikator penting yang harus dilihat kinerjanya. Data bulanan impor dan ekspor merupakan data deret waktu karena dikumpulkan, dicatat, dan diamati dalam urutan waktu. Data impor dan ekspor mengandung masalah heteroskedastisitas pada model residual dan conditional change pada volatilitas. Kombinasi model volatilitas dan Markov switching dapat mengatasi permasalahan dalam penelitian ini. Penelitian ini dikembangkan dengan menggunakan data volatilitas dan smoothed probability, selanjutnya penelitian ini memperoleh tingkat akurasi dengan membandingkan probabilitas prediksi dengan probabilitas smoothed dari data aktual. Hasil dari penelitian ini diperoleh model SWARCH(4,1) dengan ARIMA(1,0,0) untuk rata-rata dan ARCH(1) untuk varians yaitu untuk total data impor dan model SWARCH(2,1) dengan ARIMA(1, 0,0) untuk rata-rata dan ARCH(1) untuk varian yang merupakan total data ekspor. Probabilitas prediksi perbandingan dan probabilitas pemulusan dari data aktual diperoleh akurasi 40,91% untuk indikator impor dan 100% untuk indikator ekspor, artinya untuk indikator impor harus mengubah nilai awal model SWARCH agar lebih akurat.
Klasifikasi Dataset Diabetes menggunakan Algoritma K-Nearest Neighbors Musa, Fitri Diana; M. Al Haris; Purwanto, Dannu; Amri, Saeful; Fadlurohman, Alwan; Fitriyana Ningrum, Ariska
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.
DASHBOARD LINGKUNGAN HIDUP UNTUK ANALISIS DIARE MENGGUNAKAN METODE K-MEANS CLUSTERING Sitti Sahara; Amri, Saeful; Fitriyana Ningrum, Ariska; Purwanto, Dannu
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.
Application of Random Forest Method to Analyze the Effect of Smoking History on The Type and Outcomes of TB Examinations: Penerapan Metode Random Forest Untuk Menganalisis Pengaruh Riawayat Merokok Terhadap Tipe dan Hasil Pemeriksaan Pasien TBC Purwanto, Dannu; Yunanita, Novia
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.v2i2.651

Abstract

Tuberculosis (TB) continues to pose a major global health challenge, especially in developing countries. One of the key risk factors that exacerbates the condition of TB patients is smoking, which increases susceptibility to infections and worsens disease prognosis. This study aims to evaluate the influence of smoking history on the type and outcomes of TB diagnoses using a Random Forest machine learning model. The dataset comprises information from TB-diagnosed patients, including demographic details such as age, gender, smoking status, patient type, and diagnostic results. The Random Forest model achieved an accuracy of 87.36%, performing best in classifying non-TB-infected patients. However, the model struggled to accurately identify healthy individuals without TB, likely due to data imbalance. This research offers fresh insights into the potential of machine learning to enhance TB diagnosis and prevention, while deepening the understanding of smoking as a risk factor in TB management.
Co-Authors - Tarsan Agustiyar Agustiyar Ahmad Amrullah B Alfidha Rahmah Ali Imron Alwan Fadlurohman Amanda, Qorry Amri, Saeful Ariska Fitriyana Ningrum Arya Praditya Basrowi, Ray Wagiu Dhani, Oktaviana Rahma Fatkhurokhman Fauzi Haryani, Deby Aprilia Ihsan Fathoni Amri Kusuma Wardani, Amelia Lein, Raymond Bolly Linda Puspitasari M. Al Haris M. Al Haris Maryam, Adiva Kalila Miftakhul Haris Musa, Fitri Diana Prastyo, Ikwan Pratama, Rizky Adi Prizka Rismawati Arum Qonita Syalsabilla Handayani RA. Qonita Syalsabilla Handayani Rahma Nurmalita Sitti Sahara Sofa, Yulia Ratna Soffi Amalia Nur Kholifah Tiani Wahyu Utami Ulinuha, Samikoh Wardani, Amelia Kusuma Yan Nazala Bisoumi Yuli Puspita Devi Yunanita, Novia