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All Journal International Journal of Electrical and Computer Engineering IAES International Journal of Artificial Intelligence (IJ-AI) IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Rekam : Jurnal, Fotografi, Televisi Animasi SITEKIN: Jurnal Sains, Teknologi dan Industri Jurnal Teknologi Informasi dan Ilmu Komputer KLIK (Kumpulan jurnaL Ilmu Komputer) (e-Journal) Jurnal Bioedukasi JOIN (Jurnal Online Informatika) Sistemasi: Jurnal Sistem Informasi Jurnal ELTIKOM : Jurnal Teknik Elektro, Teknologi Informasi dan Komputer Informatika Mulawarman: Jurnal Ilmiah Ilmu Komputer Sinkron : Jurnal dan Penelitian Teknik Informatika Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) International Journal of Artificial Intelligence Research INTENSIF: Jurnal Ilmiah Penelitian dan Penerapan Teknologi Sistem Informasi Jurnal Sains Dan Teknologi (SAINTEKBU) CogITo Smart Journal JOURNAL OF APPLIED INFORMATICS AND COMPUTING JURNAL TEKNIK INFORMATIKA DAN SISTEM INFORMASI Applied Information System and Management ILKOM Jurnal Ilmiah Journal of Economic, Management, Accounting and Technology (JEMATech) KOMPUTIKA - Jurnal Sistem Komputer Jambura Journal of Informatics JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Bitnet: Jurnal Pendidikan Teknologi Informasi EDUMATIC: Jurnal Pendidikan Informatika METIK JURNAL Building of Informatics, Technology and Science Gema Wiralodra Indonesian Journal of Business Intelligence (IJUBI) Jurnal Tecnoscienza Generation Journal Jurnal Mnemonic Pangea : Wahana Informasi Pengembangan Profesi dan Ilmu Geografi Journal Cerita: Creative Education of Research in Information Technology and Artificial Informatics PRAJA: Jurnal Ilmiah Pemerintahan JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) Journal of Computer System and Informatics (JoSYC) JIKA (Jurnal Informatika) Community Development Journal: Jurnal Pengabdian Masyarakat Jurnal Perangkat Lunak Jurnal Informa: Jurnal Penelitian dan Pengabdian Masyarakat Jurnal TIKOMSIN (Teknologi Informasi dan Komunikasi Sinar Nusantara) Jurnal Teknologi Informatika dan Komputer Journal of Computer Networks, Architecture and High Performance Computing Jurnal Teknik Informatika (JUTIF) Jurnal Teknimedia: Teknologi Informasi dan Multimedia Journal of Electrical Engineering and Computer (JEECOM) JINAV: Journal of Information and Visualization International Journal of Artificial Intelligence and Robotics (IJAIR) Mitra Mahajana: Jurnal Pengabdian Masyarakat Jurnal Informatika dan Teknologi Komputer ( J-ICOM) DEVICE Djtechno: Jurnal Teknologi Informasi JTECS : Jurnal Sistem Telekomunikasi Elektronika Sistem Kontrol Power Sistem dan Komputer JURNAL STUDIA KOMUNIKA Jurnal Pengabdian Seni KLIK: Kajian Ilmiah Informatika dan Komputer Jurnal Saintekom : Sains, Teknologi, Komputer dan Manajemen Journal Computer Science and Informatic Systems : J-Cosys Jurnal Mandiri IT Sulawesi Tenggara Educational Journal JURNAL PAI: Jurnal Kajian Pendidikan Agama Islam Jurnal Sisfotek Global International Journal Artificial Intelligent and Informatics Jurnal Informatika Teknologi dan Sains (Jinteks) Journal of Innovation Research and Knowledge Malcom: Indonesian Journal of Machine Learning and Computer Science Nusantara of Engineering (NOE) Jurnal Bangkit Indonesia Jurnal Multidisiplin Sahombu COREAI: Jurnal Kecerdasan Buatan, Komputasi dan Teknologi Informasi JEC (Jurnal Edukasi Cendekia) Prosiding SEMNAS INOTEK (Seminar Nasional Inovasi Teknologi) SmartComp Jurnal Informatika Polinema (JIP) Jurnal Informatika: Jurnal Pengembangan IT Kesatria : Jurnal Penerapan Sistem Informasi (Komputer dan Manajemen) Scientific Journal of Informatics Pengabdian Seni Jurnal Sistem Informasi Komputer dan Teknologi Informasi Jurnal TAM (Technology Acceptance Model) Jurnal Sistem Informasi dan Teknologi Informasi Jurnal Komtika (Komputasi dan Informatika)
Kusrini, Kusrini
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Journal : DEVICE

 1 
POTENTIAL ENTRY OF DHF DISEASE BASED ON ENVIRONMENTAL CONDITIONS USING ARTIFICIAL METHODS NEURAL NETWORK PERCEPTION S, Muhammad Sabri; Herlinawati, Noor; MZ, Reza Rafiq; Kusrini, Kusrini
Device Vol 14 No 2 (2024): November
Publisher : Fakultas Teknik dan Ilmu Komputer (FASTIKOM) UNSIQ

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32699/device.v14i2.7694

Abstract

Dengue Hemorrhagic Fever (DHF) is an infectious disease caused by the dengue virus transmitted by the Aedes aegypti mosquito. The spread of DHF is greatly influenced by environmental conditions such as temperature, rainfall, humidity, and population density. In Indonesia, DHF has become a significant public health problem, especially in densely populated urban areas. Therefore, it is important to develop a predictive model that can forecast the potential occurrence of DHF based on environmental variables to reduce the impact and control the spread of this disease. The objective of this research is to develop a predictive model using the Artificial Neural Network Perception (ANN) method to predict the potential occurrence of DHF based on environmental variables, and to create an application for predicting the potential of DHF. This model is expected to help authorities make appropriate decisions to prevent and control DHF outbreaks. The research methodology includes the following stages: data collection, data preprocessing, ANN model development, model evaluation, and implementation and validation. The expected output of this research is an ANN model that can accurately predict the potential occurrence of DHF based on environmental conditions. Additionally, it is hoped that a predictive system will be available for authorities to take effective preventive and control measures against DHF. The research is expected to make a significant contribution to public health, particularly in the prevention and control of DHF. The results include an application for predicting the potential occurrence of DHF in a specific area, with features such as a Dashboard Interface, Temperature Interface, Dataset Interface, and Result Model Interface. The RMSE results obtained for this research were 0.01441372. From the research results, it can be concluded that ANN can be used to predict the potential for dengue fever to enter.
Perbandingan Kinerja Support Vector Machine dalam Klasifikasi Obesitas dengan Pendekatan Kernel Linear dan Radial Basis Function S, Muhamad Rois; Kusrini, Kusrini
Device Vol 15 No 1 (2025): Mei
Publisher : Fakultas Teknik dan Ilmu Komputer (FASTIKOM) UNSIQ

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32699/device.v15i1.8903

Abstract

Obesitas adalah kondisi medis yang ditandai dengan penumpukan lemak tubuh yang berlebihan hingga dapat menimbulkan risiko berbagai penyakit kronis, seperti diabetes, penyakit jantung, dan kanker. Di Indonesia, dalam kurun waktu 10 tahun terjadi peningkatan obesitas yang signifikan, dari 10,5% pada tahun 2007 menjadi 21,8% pada tahun 2018. Secara global, pada tahun 2030 diperkirakan 1 dari 5 wanita dan 1 dari 7 pria akan hidup dengan obesitas, yang setara dengan lebih dari 1 miliar orang di seluruh dunia. Untuk mengatasi permasalahan ini, teknologi kecerdasan buatan digunakan dalam prediksi obesitas guna mengidentifikasi faktor risiko secara lebih akurat. Penelitian ini membandingkan performa klasifikasi obesitas menggunakan algoritma Support Vector Machine (SVM) dengan dua pendekatan berbeda: SVM dengan kernel Linear tanpa hyperparameter tuning dan SVM dengan kernel Radial Basis Function (RBF) dengan hyperparameter tuning. Dataset yang digunakan bersumber dari Universitas Sinop yang tersedia di Kaggle, dengan total 1610 data. Hasil eksperimen menunjukkan bahwa model SVM Linear tanpa hyperparameter tuning memiliki akurasi 72% pada data uji, sedangkan model SVM-RBF dengan hyperparameter tuning C dan gamma mencapai akurasi 83%. Perbedaan performa ini menunjukkan bahwa pemilihan kernel dan penerapan hyperparameter tuning dapat meningkatkan akurasi serta keandalan prediksi obesitas.
Klasifikasi Penyakit Diabetes Melitus Menggunakan Metode Stacking Ensemble Herlinawati, Noor; Kusrini, Kusrini
Device Vol 15 No 1 (2025): Mei
Publisher : Fakultas Teknik dan Ilmu Komputer (FASTIKOM) UNSIQ

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32699/device.v15i1.9331

Abstract

Pendeteksian dini terhadap risiko diabetes merupakan tantangan penting dalam dunia medis modern. Penelitian ini bertujuan untuk meningkatkan akurasi klasifikasi pasien diabetes menggunakan metode stacking ensemble, yang menggabungkan tiga model pembelajaran mesin: K-Nearest Neighbors (KNN), Random Forest, dan XGBoost. Dataset yang digunakan adalah Pima Indians Diabetes, yang terdiri dari 768 data pasien. Setelah dilakukan preprocessing, balancing, dan feature selection, model stacking dibangun dengan Logistic Regression sebagai meta-learner. Hasil evaluasi menunjukkan bahwa stacking ensemble mencapai akurasi 77.27% dan ROC AUC 82.91%. Metode ini menunjukkan potensi besar dalam pengembangan sistem diagnosis otomatis yang lebih andal untuk penyakit diabetes..
POTENTIAL ENTRY OF DHF DISEASE BASED ON ENVIRONMENTAL CONDITIONS USING ARTIFICIAL METHODS NEURAL NETWORK PERCEPTION S, Muhammad Sabri; Herlinawati, Noor; MZ, Reza Rafiq; Kusrini, Kusrini
Device Vol 14 No 2 (2024): November
Publisher : Fakultas Teknik dan Ilmu Komputer (FASTIKOM) UNSIQ

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32699/device.v14i2.7694

Abstract

Dengue Hemorrhagic Fever (DHF) is an infectious disease caused by the dengue virus transmitted by the Aedes aegypti mosquito. The spread of DHF is greatly influenced by environmental conditions such as temperature, rainfall, humidity, and population density. In Indonesia, DHF has become a significant public health problem, especially in densely populated urban areas. Therefore, it is important to develop a predictive model that can forecast the potential occurrence of DHF based on environmental variables to reduce the impact and control the spread of this disease. The objective of this research is to develop a predictive model using the Artificial Neural Network Perception (ANN) method to predict the potential occurrence of DHF based on environmental variables, and to create an application for predicting the potential of DHF. This model is expected to help authorities make appropriate decisions to prevent and control DHF outbreaks. The research methodology includes the following stages: data collection, data preprocessing, ANN model development, model evaluation, and implementation and validation. The expected output of this research is an ANN model that can accurately predict the potential occurrence of DHF based on environmental conditions. Additionally, it is hoped that a predictive system will be available for authorities to take effective preventive and control measures against DHF. The research is expected to make a significant contribution to public health, particularly in the prevention and control of DHF. The results include an application for predicting the potential occurrence of DHF in a specific area, with features such as a Dashboard Interface, Temperature Interface, Dataset Interface, and Result Model Interface. The RMSE results obtained for this research were 0.01441372. From the research results, it can be concluded that ANN can be used to predict the potential for dengue fever to enter.
Co-Authors AA Sudharmawan, AA Abdillah, Yahya Auliya Abdullah Sukri, M Iqbal Abdullah, Mochamad Fadillah Achmad Oddy Widyantoro Ade Pujianto, Ade Adhani, Muhammad Azmi Agastya, I Made Artha agung budi AGUS PURWANTO Ahmad Yusuf Aji Santoso, Bayu Aji Susanto Anom Purnomo Alfatta, Hanif Alva Hendi Muhammad Andi Muhammad Irfan Andi Sunyoto Andika, Roy Andriyanto, Rifki Angga Kurniawan Anggit Dwi Hartanto, Anggit Dwi Anggraeni, Meita Dwi Ardana, Wildan Muhammad Ardana, Wildan Muhammmad Ardiansyah, Fachri Ari Yuana, Kumara Arief Setyanto Arief, M Rudyanto Arief, Muhammad Rudyanto Arifuddin, Danang Arik Sofan Tohir Aris Subadi Arli Aditya Parikesit Asnawi, Muhamad Fuat Atin Hasanah Azi, Amanda Aziz Muzani, Ma'ruf Aziz, Moh Abdul Azkar, Azkar Bayu Setiaji Béjar, Rodrigo Martínez Bentar Candra P Bernadhed, Bernadhed Bisono, Hadi Hikmadyo Braeken, An Buana, Yopy Tri Candra, Kurnia Khoirul da Silva, Bruno Darmawan, Eko Rahmad David Agustriawan DHANI ARIATMANTO Dzulhijjah, Dwi Ahmad Eko Pramono Eko Purwanto Ema Utami Emha Taufiq Luthfi Fatkhurrochman, Fatkhurrochman Fauzi, Moch Farid Fauzy, Marwan Noor Febrianti, Winda Febriyanti, Nada Rizki Ferry Wahyu Wibowo fitriyanto, nur Gifari, Okta Ihza Halimi, Ahmad Hamdikatama, Bimantyoso Hanafi Hanafi Hanif Al Fatta Hari Muktafin, Elik Haris, Ruby hartanto, david budi Hartono, Anggit Dwi Haryo, Wasis Hasan, Nur Fitrianingsih Hasan, Nurul Rahmawati Hasirun, Hasirun Helmawati, Nita Herawati, Maimi Heri Abijono, Heri Herlinawati, Noor Hulvi, Alfajri I Made Adi Purwantara Ikhwanudin, Aolia Ilmawati, Fahma Inti Indarto, Aan Jeki Kuswanto Jumaris Jumaris, Jumaris Juwariyah, Siti Kasman, Haris Saktiawan Kurniasari, Iin Kusnawi , Kusnawi Kusnawi Kusnawi Lewu, Retzi Y. Linda, Kumara Dewi Listyanto, Ahmad Wildan López, Alba Puelles Lukman Bachtiar M. RUDYANTO ARIEF M. Suyanto, M. Madhika, Yudha Randa Mahendra, Awanda Putra Mangun, Syamsul Syahab Maradona, Maradona Mardiana Mardiana Martínez-Béjar, Rodrigo Masruri, Nizar Haris Masud, Ibnu maulana, fahrizal Megantara, Muhamad Arldi MEI PARWANTO KURNIAWAN Metha, Halifa Sekar Miftachuddin, Achmad Agus Athok Mohamad Firdaus, Mohamad Mohammad Rezza Pahlevi Moningka, Nirwan Mufti Ari Bianto Muhamad Iksan, Muhamad Muhammad Resa Arif Yudianto Muktafin, Elik Hari Mulia Sulistiyono Muzakir, Muhammad MZ, Reza Rafiq Nasiri, Asro Ngaeni, Nurus Sarifatul Ni Nyoman Utami Januhari, Ni Nyoman Nugroho, Agung Nugroho, Hanantyo Sri Nuk Ghurroh Setyoningrum Nurmalasari, Maulidya Dwi Oktafiqurahman, Andi Olajuwon, Sayyid Muh. Raziq Onde, Mitrakasih La ode Oscar Samaratungga Pamoengkas, Muhamad Agoeng Pamungkas, Sapto Pradipta, Dody Prameswari, Sonia Anjani Prasetio, Agung Budi Prastyo, Rahmat Pratama, Muhammad Egy Puri, Fiyas Mahananing Purnamasari, Resti Putra, Andriyan Dwi Rachmawati Oktaria Mardiyanto RAMADHAN, SYAIFUL Rasyid, Magfirah Raynald Alfian Yudisetyanto Riduan, Nor Rizkayati, Anisa S, Muhamad Rois S, Muhammad Sabri Saleh, Robby Febrianur Samponu, Yohakim Benedictus Santosa, Hendriansyah SANTRI SANTRI Saputro, Moh. Rizal Bayu Sarawan, Tommy Sari, Yayak Kartika Selvy Megira, Selvy Semma, Andi Bahtiar Sentoso, Thedjo Setiawan, Moh. Arif Ma'ruf Setyanto, Arif Siswo Utomo, Mardi Slamet . Solikin, Arif Fajar Sudarmawan, Sudarmawan Sudarto Sudarto Swastikawati, Claudia Syafutra, Arif Dwi Syaiful Huda Tala, WD. Syarni Tampubolon, Jandri Tamuntuan, Virginia Toifur, Tubagus TONNY HIDAYAT Tri Nugroho, Arief Tukan, Ewaldus Ambrosius Ula, M. Izul Wahyu Pujiharto, Eka Wahyudi, Alfian Cahyo Wangsa, Sabda Sastra Wijaya, Jodi Wiwi Widayani, Wiwi Yanuargi, Bayu Yossy Ariyanto Yuana, Kumara Ari Yuza, Adela Zakaria Zakaria Zuhri, Muhammad Rafli Zulkarnain, Imam Alfath Zumarni, Zumarni