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All Journal IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Elkom: Jurnal Elektronika dan Komputer Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) Jurnal Teknologi Informasi dan Ilmu Komputer Jurnal Ilmiah KOMPUTASI JOURNAL OF APPLIED INFORMATICS AND COMPUTING Jiko (Jurnal Informatika dan komputer) Prosiding SNITP (Seminar Nasional Inovasi Teknologi Penerbangan) JATI (Jurnal Mahasiswa Teknik Informatika) Jifosi Jurnal Lebesgue : Jurnal Ilmiah Pendidikan Matematika, Matematika dan Statistika Jurnal Teknik Informatika (JUTIF) Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer) International Journal Of Computer, Network Security and Information System (IJCONSIST) Jurnal Sosial dan Abdimas Malcom: Indonesian Journal of Machine Learning and Computer Science Jurnal Teknik Terapan (J-TETA) Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics
idhom, Mohammad
Unknown Affiliation
Aerospace Engineering Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Health Professions Industrial & Manufacturing Engineering Materials Science & Nanotechnology Mathematics Mechanical Engineering Social Sciences Transportation

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Implementasi Algoritma LightGBM untuk Prediksi Status Gizi Bayi dan Balita di Desa Doko Kabupaten Kediri Thoriqulhaq, Muhammad; Idhom, Mohammad; Maulida Hindrayani, Kartika
Jurnal Teknik Terapan Vol. 4 No. 2 (2025): Oktober
Publisher : P3M Politeknik Negeri Jember

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

MThe issue of nutritional status among infants and toddlers remains a serious concern in Indonesia, particularly in rural areas. Doko Village was chosen as the research location due to its significant challenges in child health. This study aims to develop a nutritional status prediction model based on the LightGBM algorithm, capable of processing anthropometric data to classify nutritional categories such as "Underweight", "Normal", and "Overweight". Using an 80:20 training-to-testing data ratio, the model achieved 97% accuracy and a 94% F1-score. In addition to building the prediction model, this study also developed an interactive web application using Streamlit, and compared its results with the conventional WHO AnthroPlus method. The results indicate that LightGBM offers advantages in terms of speed, flexibility, and predictive accuracy based on local data.
Gema Gasing Aktif Sebagai Upaya Cegah Stunting Di Desa Wonokerto Khasanah, Ema Isfa'atin; Putri, Deva Amalia Rahma; Kurniawati, Dyah Ayu Listyo; Bajramaya, Dewa Widya; Idhom, Mohammad
Jurnal Sosial & Abdimas Vol. 6 No. 2 (2024): Jurnal Sosial & Abdimas
Publisher : LPPM Universitas Adhirajasa Reswara Sanjaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51977/jsa.v6i2.1726

Abstract

Sustainable Development Goals (SDGs) adalah target yang harus dicapai oleh Indonesia pada tahun 2030. Oleh karena itu, Pengabdian Masyarakat UPN Veteran Jawa Timur bertujuan untuk membantu desa-desa dalam beradaptasi dengan SDGs. Salah satu tantangan utama yang dihadapi Indonesia saat ini adalah tingginya angka stunting. Di Desa Wonokerto, terdapat 10 balita yang teridentifikasi mengalami stunting, sementara Kecamatan Wonosalam dikategorikan sebagai daerah dengan tingkat stunting yang tinggi pada tahun 2024. Program Gema Gasing Aktif (Gerakan Masyarakat Cegah Astasi Stunting dan Promosi Asi Eksklusif) dirancang untuk menyebarkan pengetahuan mengenai pencegahan stunting. Program ini terdiri dari tiga tahap utama, yaitu mendampingi ibu dan balita saat kegiatan posyandu, melakukan sosialisasi mengenai pencegahan stunting dan promosi asi eksklusif kepada remaja, ibu hamil, kader, dan orang tua sebagai upaya intervensi dini, serta memberikan pelatihan khusus kepada kader posyandu oleh BKKBN Provinsi Jawa Timur. Diharapkan bahwa program ini dapat efektif dalam mencegah stunting di Desa Wonokerto. Penelitian ini menggunakan pendekatan deskriptif dengan metode pengumpulan data melalui wawancara dan observasi.
ANALYSIS AND IMPLEMENTATION OF SENTIMENT SYSTEM ON THE ELECTABILITY OF INDONESIAN PRESIDENTIAL CANDIDATES 2024 USING SUPPORT VECTOR MACHINE METHOD Harahap, Jasmine Avrile Kaniasari; Syaifullah JS, Wahyu; Idhom, Mohammad
Jurnal Teknik Informatika (Jutif) Vol. 5 No. 4 (2024): JUTIF Volume 5, Number 4, August 2024 - SENIKO
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2024.5.4.2029

Abstract

Indonesia is a country that implements democracy in choosing presidential candidates through the election process. People have their own views on the presidential candidates they support, and in this digital era, social media is the main platform for people to express their opinions. Public opinion can be positive or negative, public opinion, hate speech, and various other comments that can cause hostility, insults, debates, and disputes. In this study, data modeling using the Support Vector Machine (SVM) method will be evaluated using a confusion matrix. The data used for anies data is 1607 tweets, prabowo data is 1761 tweets, and ganjar data is 1607 tweets with the keywords “anies baswedan”, “prabowo subianto”, and “ganjar pranowo” with the data collection period from November - December 2023. The results of this study show that the sentiment classification model has good performance. For Anies Baswedan data, the SVM model achieved accuracy of 86.64%, precision of 86.69%, recall of 86.64%, and f1-score of 86.62%. For Prabowo Subianto data, the model achieved an accuracy of 90.65%, precision of 90.81%, recall of 90.65%, and f1-score of 90.61%. Meanwhile, for Ganjar Pranowo data, the model achieved an accuracy of 93.78%, precision of 93.67%, recall of 93.78%, and f1-score of 93.72%. These results show that the system is able to classify people's sentiment.
Optimizing Clustering Analysis to Identify High-Potential Markets for Indonesian Tuber Exports Prasetya, Dwi Arman; Sari, Anggraini Puspita; Idhom, Mohammad; Lisanthoni, Angela
Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol. 7 No. 1 (2025): February
Publisher : Jurusan Teknik Elektromedik, Politeknik Kesehatan Kemenkes Surabaya, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/skzqbd57

Abstract

Agriculture is a key contributor to Indonesia's economic growth, with tubers representing the second most important food crop. Despite their significance, the export value of Indonesia’s tuber crops has not yet reached its full potential given the decline in the value of tuber exports since 2021. One of the contributing factors is the restricted range of export market options. This study aims to analyze export trade patterns to identify the most high-potential markets for Indonesian tuber commodities.  Clustering analysis is used as a key method to identify market locations by grouping countries based on similar trade characteristics. Clustering was conducted using the Gaussian Mixture Model (GMM), which enhanced by Particle Swarm Optimization (PSO) and evaluated by silhouette score and DBI. The dataset is collected from Indonesia’s Central Bureau of Statistics from 2019 to 2023, focusing on 5 kinds of tuber exports with total of 455 entries and 8 columns. Using the AIC/BIC method, the optimal number of clusters obtained is 2 which are low market opportunities (cluster 0) and high market oppurtunities (cluster 1). Results showed that the GMM model without optimization has silhouette score of 0.7602 and DBI of 0.8398, while the GMM+PSO model achieved an improved silhouette score of 0.8884 and DBI of 0.5584. Both score are categorized as strong structure but, GMM+PSO has higher silhouette score and lower DBI score, demonstrating the effectiveness of PSO in enhancing the clustering model’s performance. The key potential markets for Indonesian tuber exports are primarily concentrated in Asia, including countries such as China, Malaysia, Thailand, Vietnam, Hong Kong, and United States.
Prediksi Penyaluran Obat Kandungan Misoprostol dengan Metode Temporal Convolutional Networks Ramadani, Nurmalita; Idhom, Mohammad; Trimono
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 12 No 6: Desember 2025
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jtiik.2025126

Abstract

Aborsi ilegal di Indonesia masih menjadi permasalahan serius, terutama dengan maraknya penggunaan misoprostol yang diperjualbelikan secara ilegal. Indonesia mencatat sekitar 1,7 juta kasus aborsi per tahun, dengan 42,5 dari setiap 1.000 wanita usia subur di Pulau Jawa terlibat dalam praktik ini. Berdasarkan laporan kasus, penyalahgunaan misoprostol dapat menyebabkan komplikasi serius seperti hipertermia, hipoksia, hingga kematian akibat kegagalan multiorgan. Selain itu, ditemukan bahwa 73% obat aborsi yang dijual online mengandung misoprostol, dan lebih dari 300.000 situs penjual obat ilegal telah diblokir oleh Kementerian Komunikasi dan Informasi. Salah satu celah yang mempermudah penyalahgunaan adalah belum adanya regulasi batas kuantitas penyaluran obat tersebut. Penelitian ini menerapkan model Temporal Convolutional Networks (TCN) untuk memprediksi pola penyaluran obat misoprostol menggunakan data primer dari BPOM dengan periode 2021-2024. Hasil evaluasi menunjukkan bahwa TCN secara konsisten lebih unggul dibandingkan LSTM pada semua panjang input. TCN mencatat rata-rata penurunan NMAE sebesar 85% dan NMSE sebesar 68% dibandingkan LSTM. Pendekatan berbasis TCN ini diharapkan dapat membantu otoritas dalam meningkatkan pengawasan distribusi obat serta mendukung kebijakan pengendalian misoprostol agar tidak disalahgunakan.   Abstract Illegal abortion in Indonesia remains a serious problem, especially with the widespread use of misoprostol, which is sold illegally. Indonesia records around 1.7 million abortion cases per year, with 42.5 out of every 1,000 women of childbearing age on the island of Java involved in this practice. According to case reports, the misuse of misoprostol can lead to serious complications such as hyperthermia, hypoxia, and even death due to multi-organ failure. Additionally, it was found that 73% of abortion drugs sold online contain misoprostol, and over 300,000 illegal drug-selling websites have been blocked by the Ministry of Communication and Information. One loophole that facilitates misuse is the lack of regulations on the quantity of the drug's distribution. This study applied the Temporal Convolutional Networks (TCN) model to predict the distribution patterns of misoprostol using primary data from the Indonesian Food and Drug Administration (BPOM) for the period 2021-2024. Evaluation results show that TCN consistently outperforms LSTM across all input lengths. TCN achieves an average reduction of 85% in NMAE and 68% in NMSE compared to LSTM. This TCN-based approach is expected to assist authorities in enhancing drug distribution oversight and supporting misoprostol control policies to prevent misuse.
Prediksi Harga Saham Menggunakan ARIMA Outlier sebagai Pendekatan Awal Menuju Analisis AI Keuangan Adam, Cindi; Idhom, Mohammad; Trimono, Trimono
Elkom: Jurnal Elektronika dan Komputer Vol. 18 No. 2 (2025): Desember : Jurnal Elektronika dan Komputer
Publisher : STEKOM PRESS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/elkom.v18i2.3314

Abstract

Perkembangan kecerdasan buatan (AI) mendorong inovasi dalam analisis keuangan, termasuk prediksi harga saham yang fluktuatif. Penelitian ini bertujuan memprediksi harga saham PT Garudafood Putra Putri Jaya Tbk menggunakan model ARIMA dengan penanganan Outlier sebagai pendekatan awal menuju sistem prediksi yang lebih adaptif. Data harga penutupan harian dari Yahoo Finance dianalisis melalui uji stasioneritas, identifikasi model ARIMA, deteksi Outlier berbasis log-return, serta evaluasi performa menggunakan RMSE, MAE, dan MAPE. Hasil penelitian menunjukkan bahwa ARIMA Outlier memberikan performa lebih baik dibandingkan ARIMA dasar. ARIMA standar menghasilkan MAPE 1.32% dan AIC –899.46, sedangkan ARIMA dengan tiga dummy Outlier mencapai MAPE 1.16% dan AIC –900.37. Peramalan 14 hari ke depan menunjukkan pola yang stabil pada kisaran Rp 370–371. Pada data uji, ARIMA dasar memberikan akurasi terbaik pada pertengahan Agustus, sedangkan ARIMA Outlier mencapai akurasi tertinggi pada akhir Agustus dengan prediksi Rp 370.2 yang sangat dekat dengan harga aktual Rp 370.4. Hasil ini menunjukkan bahwa penanganan Outlier meningkatkan ketepatan model, sehingga ARIMA Outlier dapat digunakan sebagai fondasi awal menuju pengembangan sistem prediksi keuangan berbasis AI.
DEPLOYMENT DETEKSI KEMATANGAN BUAH KELAPA SAWIT BERBASIS YOLOV11 DENGAN ONNX RUNTIME DAN STREMLIT Ramadhan Anniswa, Iqbal; Syaifullah J. S, Wahyu; Idhom, Mohammad; Rizaldy Pratama, Alfan; Gede Susrama Mas Diyasa, I
Prosiding SNITP (Seminar Nasional Inovasi Teknologi Penerbangan) Vol. 9 No. 1 (2025): SNITP 2025
Publisher : Politeknik Penerbangan Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Kelapa Sawit merupakan komoditas strategis di Indonesia yang menjadi salah satusumber devisa utama. Tingkat kematangan buah kelapa sawit sangat berpengaruhterhadap kualitas minyak yang dihasilkan, sehingga diperlukan metode yang cepat,tepat, dan konsisten untuk mendeteksi tingkat kematangan buah. Dalam metedoKonversional masih mengandalkan pengamatan visual oleh pekerja lapangan seringbersifat subjektif dan tidak efesien.Dengan hal tersebut,penelitian ini mengusulkanpenerapan model object detection berbasis YOLOv11 untuk mendeteksikematangan buah kelapa sawit. Model YOLOv11 dipilih karena memilikikeunggulan dalam kecepatan inferensi dan akurasi deteksi pada objek kecil maupunkompleks. Untuk memfasilitasi penggunaan di lingkungan produksi,Model yangtelah dilatih dikonversi ke format ONNX dan dijalankan menggunakan ONNXRuntime agar memperoleh perfoma inferensi yang lebih optimal pada sumber dayaterbatas. Selanjutnya, aplikasi antarmuka berbasis Streamlit dikembangkan untukmemudahkan pengguna dalam mengunggah gambar atau video dan memperolehhasil deteksi secara real-time. Diharapkan, sistem ini mampu memberikan solusipraktis, efisien, dan akurat dalam mendukung proses panen buah kelapa sawit.
Fuzzy Time Series Cheng Optimasi Adaptive Particle Swarm Optimization (APSO) untuk Optimalisasi Prediksi Harga Beras di Kota Surabaya Ulayya, Yasmin; Idhom, Mohammad; Diyasa, I Gede Susrama Mas
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 13 No 1: Februari 2026
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jtiik.2026131

Abstract

Harga beras rentan mengalami fluktuasi, berdampak signifikan pada kesejahteraan masyarakat, terutama kelompok berpendapatan rendah. Di Surabaya, kenaikan harga beras mendorong perlunya prediksi akurat untuk mitigasi dampak ekonomi. Penelitian ini bertujuan meramalkan harga beras menggunakan metode Fuzzy Time Series Cheng (FTS Cheng) yang dioptimalkan dengan Adaptive Particle Swarm Optimization (APSO) untuk menangani data non-linear dan fluktuatif. Data sekunder diambil dari Dinas Perindustrian dan Perdagangan Provinsi Jawa Timur (Siskaperbapo) periode 1 Januari 2023 hingga 31 Maret 2025, mencakup harga beras premium dan medium di Pasar Tambahrejo dan Pasar Wonokromo. Metode utama adalah FTS Cheng dengan optimasi APSO untuk meningkatkan akurasi prediksi. Model menunjukkan akurasi tinggi dengan MAPE (Mean Absolute Percentage Error) sangat rendah. Di Pasar Tambahrejo, MAPE beras premium 0,09% dan medium 0,00%. Di Pasar Wonokromo, MAPE premium 6,38% dan medium 0,85%. Optimasi APSO berhasil menurunkan MAPE, misalnya di Pasar Tambahrejo (premium turun 0,38%, medium turun 0,68%). Kombinasi FTS dan APSO menghasilkan prediksi harga beras yang presisi. Temuan ini dapat mendukung kebijakan stabilisasi harga, manajemen stok, dan perencanaan produksi beras lebih efektif, sekaligus meningkatkan stabilitas ekonomi rumah tangga.   Abstract Rice prices are prone to fluctuations, significantly impacting public welfare, especially low-income groups. In Surabaya, rising rice prices necessitate accurate predictions to mitigate economic impacts. This research aims to forecast rice prices using the Fuzzy Time Series Cheng (FTS Cheng) method optimized with Adaptive Particle Swarm Optimization (APSO) to handle non-linear and fluctuating data. Secondary data was obtained from the East Java Provincial Department of Industry and Trade (Siskaperbapo) for the period January 1, 2023, to March 31, 2025, covering premium and medium rice prices at Tambahrejo Market and Wonokromo Market. The main method is FTS Cheng with APSO optimization to improve prediction accuracy. The model demonstrates high accuracy with very low MAPE (Mean Absolute Percentage Error). At Tambahrejo Market, MAPE for premium rice is 0.09% and medium rice is 0.00%. At Wonokromo Market, MAPE for premium rice is 6.38% and medium rice is 0.85%. APSO optimization successfully reduces MAPE, for example at Tambahrejo Market (premium decreased by 0.38%, medium decreased by 0.68%). The combination of FTS Cheng and APSO produces precise rice price predictions. These findings can support price stabilization policies, stock management, and more effective rice production planning, while improving household economic stability.
Implementasi Extremely Randomized Trees dengan Optimasi Hyperparameter Accelerated Particle Swarm Optimization untuk Klasifikasi Subtipe Anemia Adelia, Adelia; Trimono, Trimono; Idhom, Mohammad
JURNAL FASILKOM Vol. 16 No. 1 (2026): Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer)
Publisher : Unversitas Muhammadiyah Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37859/jf.v16i1.11295

Abstract

Anemia is a health problem that negatively affects both medical outcomes and social well-being, highlighting the need for accurate early detection. This study applies a machine learning approach to classify anemia subtypes to support clinical intervention and further examination. The Extra Trees method employs a hierarchical decision-tree structure with extreme randomization, making it robust to overfitting and capable of good generalization on small to medium datasets. Accelerated Particle Swarm Optimization (APSO) is utilized as an efficient optimization technique to improve classification performance. The novelty of this study lies in integrating Extra Trees with APSO to optimize anemia subtype classification. The dataset consists of 385 records collected from a regional hospital in East Java, Indonesia, covering four classes: thalassemia, iron deficiency anemia, anemia of chronic disease, and non-anemia. The features include patient initials, gender, age, and hematological parameters (Hb, HCT, RBC, MCV, MCH, MCHC, RDW). The optimized model achieved 85% accuracy, 87% precision, 85% recall, 85% F1-score, 95% specificity, and 94% AUC, outperforming the non-optimized model. These results indicate that the proposed approach is effective for anemia subtype classification.
Co-Authors Adam, Cindi Adelia Adelia, Adelia Alfan Rizaldy Pratama Alif, Rahmat Istighfaroni Aminullah, Ahmad Adiib Angga, Angga Rahmad Purnama Anggraini Puspita Sari Anniswa, Iqbal Ramadhan Azzahra, Adelia Ramadhina Bajramaya, Dewa Widya Basuki Rahmat Masdi Siduppa Cahaya Purtri Agustika Carissa, Savvy Prissy Amellia Diash, Hakam Dzakwan Diyasa, I Gede Susrama Mas Dwi Arman Prasetya Fahrudin, Tresna Maulana Gede Susrama Mas Diyasa, I Harahap, Jasmine Avrile Kaniasari Henni Endah Wahanani JS, Wahyu Syaifullah Kartika Maulida Hindrayani Khasanah, Ema Isfa'atin Kurniawati, Dyah Ayu Listyo Lidya Musaffak, Awal Linggasari, Dienna Eries Lisanthoni, Angela Maulana, Hendra Muhammad Rizki Alamsyah Nabila, Nasywa Azzah Nafiah, Fajria Ulumin Nariyana, Calvien Danny Nathania, Vannesa naufal firdaus, ahmad Panglima, Talitha Fujisai Prismahardi Aji Riyantoko Putri, Deva Amalia Rahma Ramadani, Nurmalita Ramadhan Anniswa, Iqbal Ryan Dana, Alvin Shaffa Ameera, Divanda Sugiarti, Nova Putri Dwi Susrama Mas Diyasa, I Gede Syaifullah J. S, Wahyu Syaifullah JS, Wahyu Terza Damaliana, Aviolla Thohir, A. Zaki Thoriqulhaq, Muhammad Trimono Trimono, Trimono Ulayya, Yasmin Wahyu Syaifullah Jauharis Saputra Widi Saputro, Tegar