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All Journal International Journal of Electrical and Computer Engineering IAES International Journal of Artificial Intelligence (IJ-AI) TEKNIK INFORMATIKA Jurnal Sains dan Teknologi Jurnal Simetris Bulletin of Electrical Engineering and Informatics Jurnal Teknologi Informasi dan Ilmu Komputer Sistemasi: Jurnal Sistem Informasi Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) International Journal of Artificial Intelligence Research JITK (Jurnal Ilmu Pengetahuan dan Komputer) Jurnal Sisfokom (Sistem Informasi dan Komputer) J-SAKTI (Jurnal Sains Komputer dan Informatika) KOMIK (Konferensi Nasional Teknologi Informasi dan Komputer) Jurnal Seni Tari Journal of Computer System and Informatics (JoSYC) Hanifiya: Jurnal Studi Agama-Agama Jurnal Teknik Informatika (JUTIF) Journal of Applied Data Sciences Jurnal Manajemen Informatika Jayakarta Bulletin of Computer Science Research J-SAKTI (Jurnal Sains Komputer dan Informatika) BEES: Bulletin of Electrical and Electronics Engineering JOMLAI: Journal of Machine Learning and Artificial Intelligence Jurnal: International Journal of Engineering and Computer Science Applications (IJECSA) Journal of Artificial Intelligence and Digital Business Bulletin of Artificial Intelligence Ulil Albab Jurnal Manajemen, Pendidikan dan Ilmu Komputer (JMENDIKKOM)
Solikhun Solikhun, Solikhun
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Religion Arts Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Materials Science & Nanotechnology Social Sciences Other

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The Utilization Of The Conjugate Gradient Algorithm For Predicting School Year Expectations By Province Simbolon, Astri Rismauli; Solikhun, Solikhun
International Journal of Engineering and Computer Science Applications (IJECSA) Vol. 2 No. 1 (2023): March 2023
Publisher : Universitas Bumigora Mataram-Lombok

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/ijecsa.v2i1.2426

Abstract

Expected Length of School (HLS) is the length of school (in years) that is expected to be felt by children at a certain age in the future. It is assumed that the probability that the child will remain in school at the following ages is the same as the probability of the population attending school per total population for the current age. Length of School is also a benchmark for evaluating government programs in improving Human Resources that excel in the competition of technological advances. The purpose of this study is to apply the Conjugate Gradient Algorithm with the Best Performance for Predicting School Life Expectancy in Indonesia. Research data on the Expectation of Schooling in Indonesia consists of 10 Provinces obtained from the Central Statistics Agency from 2016 to 2021. This study uses 5 architectural models, namely 2-10-1, 2-15-1, 2-20-1, 2-25-1 and 2-30-1. Of the five architectural models used, the best architectural model is 2-3-1 with an MSE of 0.000000002 in two seconds. Based on this best architectural model, it will be used to predict the Expectation of Old Schools in Indonesia for the next five years, namely from 2022 to 2026.
SISTEM PENDUKUNG KEPUTUSAN DALAM PEMILIHAN KETUA OSIS DI SMK SWASTA ISLAM PROYEK UISU SIANTAR DENGAN MENGGUNAKAN METODE SAW Indri, Indriyani; Wika, Wika Kasanova; Solikhun, Solikhun
Jurnal Manajemen, Pendidikan Dan Ilmu Komputer Vol. 1 No. 1 (2024): JMENDIKKOM Volume 1 No 1 Januari 2024
Publisher : Yayasan Darus Soleh Parung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.65309/84yx1f56

Abstract

OSIS atau kepanjangan dari Organisasi Siswa Intra Sekolah merupakan organisasi tertinggi di sekolah yang berfungsi untuk menggerakkan siswa agar dapat berkontribusi dalam berbagai aktvitas yang mendukung sekolah. SMK Swasta Islam Proyek UISU Siantar juga memiliki OSIS yang dilakukan siswa dalam menjalankan kegiatan-kegiatan yang melibatkan siswa. Tujuan dalan penelitian ini adalah untuk melakukan pemilihan Ketua OSIS di SMK Swasta Islam Proyek UISU Siantar dengan menggunakan Sistem Pendukung Keputusan dengan Algoritma SAW. Adapun data penelitian ini sebanyak 9 siswa calon yang akan terpilih menjadi Ketua OSIS. Penelitian ini menghasilkan urutan tertinggi pertama diperoleh A6 = Bima dengan hasil 92,1007 yang dakan menjadi calon Ketua OSIS dari hasil Algoritma SAW. Dengan hasil penelitian ini dapat memberikan keputusan yang akan diambil oleh pihak Sekolah dan siswa di SMK Swasta Islam Proyek UISU Siantar. 
Implementasi Algoritma Backpropagation Dalam Prediksi Laju Pertumbuhan Penduduk Di Kabupaten Sinjai Napitupulu, Jessica Evonella; Solikhun, Solikhun
Jurnal Manajemen, Pendidikan Dan Ilmu Komputer Vol. 1 No. 1 (2024): JMENDIKKOM Volume 1 No 1 Januari 2024
Publisher : Yayasan Darus Soleh Parung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.65309/evgesw76

Abstract

Indonesia merupakan salah satu negara yang mempunyai jumlah penduduk terbanyak di dunia sekitar dua ratus juta jiwa dan menduduki urutan keempat setelah Amerika Serikat dalam daftar jumlah penduduk terbanyak di dunia. Pertumbuhan penduduk Indonesia semakin meningkat dari tahun ke tahun, dan wilayah serta kota menjadi semakin padat penduduknya. Semakin bertambah jumlahnya maka luas wilayah pun semakin berkurang akibat kepadatan penduduk. Penelitian ini membahas tentang penerapan Algoritma Backpropagation dalam prediksi  laju pertumbuhan penduduk di Kabupaten Sinjai. Tujuan dari penelitian ini adalah untuk melakukan prediksi yang dapat membantu pemerintah daerah dalam perencanaan pembangunan dan pengelolaan sumber daya yang lebih efektif. Metode Backpropagation digunakan dalam pelatihan model jaringan syaraf tiruan dengan menggunakan data historis jumlah penduduk serta faktor-faktor yang mempengaruhi pertumbuhan tersebut. Tulisan ini memaparkan hasil penelitian yang bertujuan untuk menerapkan algoritma Backpropagation dalam  upaya memprediksi laju pertumbuhan penduduk di Kabupaten Sinjai dari tahun 2013-2021 dengan menggunakan Microsoft Excel dan Matlab versi 2011b untuk pengolahan dan analisis data. Arsitekturnya menggunakan tiga model, yaitu: 4-5-1, 4-10-1, 4-10-1. Model arsitektur yang paling akurat adalah model 4-10-1 yang memiliki Mean Squared Error (MSE) sebesar 0,00000024 dan tingkat akurasi 100% dengan waktu 00:07 pada epoch 247.
OPTIMIZING SHUFFLENET WITH GRIDSEARCHCV FOR GEOSPATIAL DISASTER MAPPING IN INDONESIA Ahmad, Abdullah; Hartama, Dedy; Solikhun, Solikhun; Poningsih, Poningsih
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 11 No. 2 (2025): JITK Issue November 2025
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v11i2.6747

Abstract

Accurate classification of natural disasters is crucial for timely response and effective mitigation. However, conventional approaches often suffer from inefficiency and limited reliability, highlighting the need for automated deep learning solutions. This study proposes an optimized Convolutional Neural Network (CNN) based on the lightweight ShuffleNet architecture, enhanced through GridSearchCV for systematic hyperparameter tuning. Using a geospatial dataset of 3,667 images representing earthquake, flood, and wind-related disasters in Indonesia, the optimized ShuffleNet model achieved a peak accuracy of 99.97%, outperforming baseline CNNs such as MobileNet, GoogleNet, ResNet, DenseNet, and standard ShuffleNet. While these results demonstrate the potential of combining lightweight architectures with automated optimization, the exceptionally high performance also indicates possible risks of overfitting and dataset bias due to limited variability. Therefore, future research should validate this approach using larger, multi-source datasets to ensure robustness and real-world applicability
OPTIMIZING SHUFFLENET WITH GRIDSEARCHCV FOR GEOSPATIAL DISASTER MAPPING IN INDONESIA Ahmad, Abdullah; Hartama, Dedy; Solikhun, Solikhun; Poningsih, Poningsih
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 11 No. 2 (2025): JITK Issue November 2025
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v11i2.6747

Abstract

Accurate classification of natural disasters is crucial for timely response and effective mitigation. However, conventional approaches often suffer from inefficiency and limited reliability, highlighting the need for automated deep learning solutions. This study proposes an optimized Convolutional Neural Network (CNN) based on the lightweight ShuffleNet architecture, enhanced through GridSearchCV for systematic hyperparameter tuning. Using a geospatial dataset of 3,667 images representing earthquake, flood, and wind-related disasters in Indonesia, the optimized ShuffleNet model achieved a peak accuracy of 99.97%, outperforming baseline CNNs such as MobileNet, GoogleNet, ResNet, DenseNet, and standard ShuffleNet. While these results demonstrate the potential of combining lightweight architectures with automated optimization, the exceptionally high performance also indicates possible risks of overfitting and dataset bias due to limited variability. Therefore, future research should validate this approach using larger, multi-source datasets to ensure robustness and real-world applicability
Co-Authors Abdullah Ahmad Agus Perdana Windarto Aisyah, Nurfahana Siti Aisyah, Nurfahana Siti Akbari, Imam Alifah, Winanda Alkhairi, Putrama Amalya, Nanda Ambariyanto Ambariyanto Amri Amri Apriliani, Lulu Ardha, Mhd. Zoel Arifin Nur, Khairun Nisa Astuti, Wiwik Sri Batubara, Ela Roza Bintang Hanggoro Putra Carissa Arishandy, Dinda Dani, Siska Rama DENY KURNIAWAN Dian Rahayu Fatmawati, Kiki Fitri Rahayu Futihatus Sirriyah Gusfirullah, Icmi Hartama, Dedy Ht. Barat, Ade Ismiaty Ramadhona Hutasoit, Rahel Adelina Hutasoit, Rahel Adelina Imam Subekti Indri, Indriyani Iwan Purnama Kurniawan Kurniawan Kusuma, Rizky Tri Lubis, Ega Khairunnisa Lubis, Ega Khairunnisa Mochamad Wahyudi Muhammad Ilham Muhammad Raqib Syahkur Muhammad Ridwan Lubis Muliadi Napitupulu, Jessica Evonella Nasution, Della Fatricia Oktaviani, Selli Poningsih Poningsih Poningsih, Poningsih Pujiastuti, Lise Putri, Adelia Rafai, Muhammad Rahman Rahman Rahmat Widia Sembiring - Ramahdhani, Dwi Revi, Ahmad Revi, Ahmad Safii, M Safii, M Safii, M. Saputra, Widodo Simbolon, Astri Rismauli Siregar, Muhammad Rahmansyah Siregar, Sandy Putra Sitompul, Wati Rizky Pebrianti Sormin, Rizky Kairunnisa Sundari Retno Andani Trianda, Dimas Trisno, Agus Wanto, Anjar Widodo Saputra Wika, Wika Kasanova Yasin, Verdi