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All Journal TEKNIK INFORMATIKA Prosiding Seminar Nasional Sains Dan Teknologi Fakultas Teknik Jurnal Ilmiah Kursor JOIV : International Journal on Informatics Visualization JURNAL MEDIA INFORMATIKA BUDIDARMA JURNAL TEKNIK INFORMATIKA DAN SISTEM INFORMASI MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer CYBERNETICS KOMPUTEK Jurnal Elektronika Listrik dan Teknologi Informasi Terapan Generation Journal Jurnal Teknika JOURNAL OF INFORMATION SYSTEM MANAGEMENT (JOISM) Jurnal Berdaya Mandiri Jurnal Teknik Informatika (JUTIF) Joutica : Journal of Informatic Unisla INFORMATIKA Jurnal Ilmiah Edutic : Pendidikan dan Informatika Journal of Artificial Intelligence and Digital Business Jurnal Abdimas Terapan Dedication : Jurnal Pengabdian Masyarakat Journal of Mathematics, Computation and Statistics (JMATHCOS)
Miftahus Sholihin
Universitas Islam Lamongan
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Peningkatan Kualitas Guru Melalui Komunitas: Pelatihan dan Pendampingan Penyusunan Perangkat Pembelajaran Untuk Mendukung SDGs-4 Septaria, Kiki; Fatharani, Atika; Sholihin, Miftahus; Kholiq, Abdul; Zamroni, Moh. Rosidi; Hendratmoko, Ahmad Fauzi; Hayati, Erna; Azizah, Luluk Nur; Leksana, Dinar Mahdalena
Jurnal Abdimas Terapan Vol. 4 No. 2 (2025): JURNAL ABDIMAS TERAPAN (MEI)
Publisher : Program Vokasi Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56190/jat.v4i2.84

Abstract

Peningkatan kualitas guru merupakan elemen krusial dalam mewujudkan pendidikan berkualitas yang sejalan dengan prinsip SDGs-4. Meski demikian, banyak guru menghadapi tantangan dalam menyusun perangkat pembelajaran berbasis Kurikulum Merdeka, terutama dalam mengintegrasikan nilai-nilai keberlanjutan, inklusivitas, dan teknologi pembelajaran. Kegiatan pengabdian kepada masyarakat ini bertujuan untuk memberikan pelatihan dan pendampingan kepada guru di Madrasah Aliyah Negeri (MAN) 1 Lamongan dalam menyusun perangkat pembelajaran yang relevan dan inovatif. Metode kegiatan mencakup identifikasi kebutuhan guru, pelatihan intensif, pendampingan berkelanjutan, evaluasi, serta publikasi dan penyebarluasan hasil kegiatan. Dampak dari kegiatan ini menunjukkan bahwa kompetensi guru dalam menyusun perangkat pembelajaran yang sesuai dengan Kurikulum Merdeka telah meningkat secara signifikan. Terdapat 90% guru berhasil membuat perangkat pembelajaran yang berkualitas, serta 75% guru mampu meningkatkan keterampilan teknologinya. Kegiatan ini juga menciptakan dampak jangka panjang dengan membentuk komunitas belajar guru sebagai wadah pengembangan profesional berkelanjutan dan penyebaran praktik terbaik kepada guru-guru lain di wilayah sekitar. Manfaat dari kegiatan ini tidak hanya memperkuat kapasitas guru di MAN 1 Lamongan, tetapi juga memberikan kontribusi signifikan terhadap pengembangan pendidikan berbasis SDGs-4 di tingkat lokal dan nasional.
PERAMALAN SUHU UDARA MENGGUNAKAN ALGORITMA LONG SHORT TERM MEMORY Yulian Ansori; Arief Rahman; Febriyanti Darnis; Miftahus Sholihin
Informatika: Jurnal Teknik Informatika dan Multimedia Vol. 5 No. 1 (2025): MEI : JURNAL INFORMATIKA DAN MULTIMEDIA
Publisher : LPPM Politeknik Pratama Kendal

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/informatika.v5i1.1033

Abstract

This study presents an air temperature forecasting model employing the Long Short-Term Memory (LSTM) algorithm to address the challenges posed by climate variability and extreme weather conditions. Historical daily temperature data from NASA POWER—collected between January 1, 2014, and December 31, 2024, in Serang City (totaling 4,018 records)—were used. The data were normalized using a min–max scaling technique and divided into training (70%) and testing (30%) sets. Multiple experimental scenarios were run by varying the number of training epochs and the hidden layer unit counts. The optimal configuration was achieved in Scenario 7, which incorporated two hidden layers, each with 50 units, and employed 30 epochs; this setup yielded a prediction accuracy of 98.4% with a Root Mean Squared Error (RMSE) of 27.11. The results indicate that the LSTM model effectively captures the seasonal variations and long-term trends in air temperature, making it a reliable tool for forecasting and supporting decision-making in climate adaptation strategies. Keywords: Air Temperature Forecasting, Long Short-Term Memory, Deep Learning, Climate Change, Data Normalization.
Membangun Dashboard Analisis Perilaku Konsumen dengan pendekatan Market Basket Analysis Sholihin, Miftahus; Sari, Putri Dina; Ikhsan, Aulia; Rahman, Arief
RIGGS: Journal of Artificial Intelligence and Digital Business Vol. 4 No. 2 (2025): Mei - Juli
Publisher : Prodi Bisnis Digital Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/riggs.v4i2.662

Abstract

Pada dasarnya dalam suatu bisnis data ada setiap harinya, namun yang harus dipikirkan adalah bukan seberapa banyak kuantitas data tersebut namun lebih ke arah pengelolaan data yang mana bisa bermanfaat untuk perkembangan bisnis. Saat ini, kegiatan marketing bergantung pada data untuk menganalisis dan memahami perilaku konsumen sekaligus memberikan wawasan yang jelas tentang produk atau layanan manakah yang paling populer. Salah satu metode yang dapat digunakan untuk menganalisis pola perilaku belanja konsumen adalah Market Basket Analysis. Analisis ini merupakan salah satu metode dalam penambangan data (data mining) yang bertujuan untuk menemukan produk-produk yang sering dibeli bersamaan dari data transaksi. penelitian ini bertujuan untuk membangun Dashboard Analytics berdasarkan Market Basket Analysis yang mudah digunakan oleh industri ritel dalam pengambilan keputusan agar meningkatkan penetrasi pasar nasional menggunakan software R Studio. Penerapan algoritma apriori pada aplikasi Dashobard Market Analysis lebih efisien dan dapat mempercepat proses pembentukan kecenderungan pola kombinasi itemset hasil penjualan produk-produk barang. Sistem aplikasi ini telah dibuat untuk memudahkan para pelaku bisnis untuk mengoptimalkan penjualan. Manager suatu swalayan dapat mengatur dan mengoptimalakan posisi produk dalam rak berdasarkan hasil analisis perilaku konsumen menggunakan metode Market Basket Analysis.
A Novel CNN-Based Approach for Classification of Tomato Plant Diseases Sholihin, Miftahus; Bin Md. Fudzee, Mohd Farhan; Anifah, Lilik
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol. 24 No. 3 (2025)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v24i3.4464

Abstract

Tomatoes are one of the most widely cultivated and consumed crops globally, but they are highly susceptibleto various diseases that can significantly reduce yield and quality. Early detection of thesediseases is crucial for effective management and prevention. The objective of this study is to developan accurate early detection system for tomato diseases using deep learning to support effective cropmanagement. The research method employed is a modified Convolutional Neural Network trainedon the PlantVillage dataset, which consists of 21,000 images across 10 disease classes. The studyevaluates three training scenarios using different epoch values (25, 50, and 75) to optimize modelperformance. Data preprocessing included image resizing and augmentation, followed by ConvolutionalNeural Network training and validation. The study’s results showed that increasing epochsimproved the model’s accuracy: 98.18% at 25 epochs, 98.53% at 50 epochs, and 99.19% at 75 epochs.Precision, recall, and F1-score also increased, from 90.95% at 25 epochs to 95.80% at 75 epochs, indicatingenhanced model reliability. However, longer training times were required as the epoch countincreased. This research concludes that a modified Convolutional Neural Network can accuratelyclassify tomato diseases, providing a reliable and practical tool for early disease detection. The proposedsystem has the potential to be integrated into mobile applications for real-time use in the field.It contributes to sustainable agriculture by enabling timely disease intervention and improving cropproductivity.
Stability Analysis and Estimation of the Basic Reproductive Ratio Using a SEITA-Type Model of HIV/AIDS Spread in Cilegon City Mahuda, Isnaini; Sholihin, Miftahus; Sonda, Atia; Sari, Putri Dina; Asshiddieqie, Rafi Ramadhan; Udiansyah, Naufal Arrafi
Journal of Mathematics, Computations and Statistics Vol. 8 No. 2 (2025): Volume 08 Nomor 02 (Oktober 2025)
Publisher : Jurusan Matematika FMIPA UNM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35580/jmathcos.v8i2.9586

Abstract

HIV/AIDS remains a critical global health issue that requires a multidisciplinary approach to reduce its transmission. Understanding the transmission dynamics through mathematical models can assist in formulating effective intervention strategies. This study aims to analyze the stability of HIV/AIDS transmission model in Cilegon City using five compartments, namely Susceptible, Educated, Infected, Treatment, and AIDS or SEITA-type model. Subsequently, the basic reproductive ratio (R0) is estimated by constructing the Next Generation Matrix (NGM) and the dynamic simulation of the model is carried out using parameters calibrated based on HIV/AIDS data from Cilegon City. The Analysis of stability equilibrium points show that the disease-free equilibrium point is locally asymptotically stable when R0<1 and when R0>1 then endemic equilibrium point is locally asymptotically stable. Furthermore, the numeric simulation results indicate that the increasing parameter transition rate from the susceptible subpopulation to the educated subpopulation, the ARV treatment rate applied to the infected subpopulation and decreasing parameter transition rate from the educated subpopulation to the susceptible subpopulation, could suppress the basic reproduction number, thereby enabling effective control of the HIV/AIDS spread in Cilegon City.
Evaluating BiLSTM  Performance with BERT, RoBERTa, and DistilBERT in Online Bullying News Detection Zamroni, Moh. Rosidi; Sholihin, Miftahus; Hayati, Erna; Rahayu A Hamid; Nurul Aswa Omar
JURNAL TEKNIK INFORMATIKA Vol. 18 No. 2: JURNAL TEKNIK INFORMATIKA
Publisher : Department of Informatics, Universitas Islam Negeri Syarif Hidayatullah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15408/jti.v18i2.42459

Abstract

This study examines the performance of BiLSTM combined with three transformer-based word embeddings—BERT, RoBERTa, and DistilBERT—in classifying bullying news in online media. BiLSTM was chosen for its significant advantages in processing text sequences compared to traditional RNN and LSTM models. The study used a dataset of 2,800 articles from three major Indonesian news portals, with 2,000 articles for training and 800 for testing, labeled using the lexicon method. The testing results showed that the combination of BiLSTM and RoBERTa achieved the best performance, with an accuracy of 94% and a near-perfect precision of 99%. Statistical significance tests confirmed that BiLSTM with RoBERTa performs significantly better than with BERT or DistilBERT. These findings suggest that the BiLSTM and RoBERTa combination is the most effective for classifying bullying news, especially for new or unseen data. This research contributes to the development of automatic bullying content detection systems to enhance content moderation on news platforms.
DETEKSI DEPRESI PADA SISWA BERBASIS WEB (STUDI KASUS: MTs TERPADU RAUDLATUL QUR’AN LAMONGAN) Sholihin, Miftahus; Leksana, Dinar Mahdalena; Hayati, Erna; Kholiq, Abdul; Zamroni, M. Rosidi; Anam, M. Khairul; Sulaiman, Akhmad Nurali; Umam, Moch. Zuhrul; Fatkhul U, M. Miftah; Prastowo, Diko
Jurnal Abdimas Terapan Vol. 3 No. 2 (2024): JURNAL ABDIMAS TERAPAN (MEI)
Publisher : Program Vokasi Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56190/jat.v3i2.58

Abstract

This Students' mental health in the school environment is becoming increasingly important. Depression, as a severe mental health disorder, can have a negative impact on students' well-being and academic achievement if it is not detected and treated appropriately and quickly. This service program aims to detect depression in MTs Roudlatul Qur'an students as early as possible to reduce the risk of ongoing depression. This can be done by building a website-based system that students can use to detect depression independently. This system was created to make it easier for students to recognize the early symptoms of depression by selecting the symptoms according to what they are experiencing. With this system, it is hoped that it can prevent depression as early as possible for students and teachers quickly and more efficiently, thereby reducing students who are at risk of experiencing depression. Apart from that, the system provides valuable information for students and teachers to understand depression more deeply and find ways to overcome it. Through this program, it is hoped that awareness of the importance of mental health in the school environment can increase and create a healthier and more supportive learning environment for all students.
Sistem Pendukung Keputusan Pemilihan Supplier Frozen Food Menggunakan Metode Topsis (Studi Kasus: UD. MHA Frozen Food) Surojuddin, Eko; Sholihin, Miftahus; Setia Budi, Agus
KOMPUTEK Vol. 9 No. 2 (2025): Oktober
Publisher : Universitas Muhammadiyah Ponorogo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24269/jkt.v9i2.3473

Abstract

MHA Frozen Food adalah perusahaan yang berfokus pada distribusi produk makanan beku, sehingga pemilihan pemasok yang tepat menjadi faktor krusial untuk menjaga mutu, ketersediaan, dan keberlangsungan bisnis. Penelitian ini bertujuan mengembangkan sistem pendukung keputusan dalam pemilihan supplier dengan menerapkan metode Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) berdasarkan empat kriteria utama, yakni Harga (35), Kualitas (30), Pengiriman (20), dan Layanan (15). Data dari beberapa alternatif pemasok diolah melalui tahapan metode TOPSIS, mulai dari penyusunan matriks keputusan yang dinormalisasi, pembobotan, hingga perhitungan kedekatan terhadap solusi ideal positif dan negatif untuk memperoleh nilai preferensi akhir. Hasil analisis menunjukkan bahwa pemasok dengan peringkat tertinggi adalah FIESTA (A7) dengan skor 0,7407, kemudian CHAMP (A2) dengan nilai 0,6653, serta KIMBO (A5) dengan 0,5994, sementara SO GOOD (A6) berada di posisi terakhir dengan skor 0,1099. Temuan ini menegaskan bahwa penerapan TOPSIS mampu memberikan hasil yang efektif, objektif, dan transparan sebagai dasar perusahaan dalam menentukan supplier paling sesuai dengan kebutuhannya. Kata Kunci : Supplier, Frozen Food, TOPSIS, Pengambilan Keputusan
Fine-Tuned Transfer Learning with InceptionV3 for Automated Detection of Grapevine Leaf Diseases Sholihin, Miftahus; Zamroni, Moh. Rosidi; Anifah, Lilik; Fudzee, Mohd Farhan Md; Ismail, Mohd Norasri
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 5 (2025): JUTIF Volume 6, Number 5, Oktober 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.5.4717

Abstract

Grape leaf diseases pose a major threat to vineyard productivity, making early and accurate detection essential for modern grape plantation management. Despite advancements in computer vision, challenges remain in differentiating diseases with visually similar symptoms. This study addresses that gap by developing a grape leaf disease classification system using a fine-tuned deep learning model based on the InceptionV3 architecture. Three training scenarios were conducted with fixed parameters batch size of 32 and learning rate of 0.001while varying the number of epochs (25, 50, and 75). Results showed a consistent improvement in classification accuracy with increased training epochs, reaching 98.64%, 98.78%, and 99.09% respectively. Confusion matrix analysis revealed that most misclassifications occurred between visually similar diseases such as Black Rot and ESCA, but error rates declined as the number of epochs increased. Rather than merely applying transfer learning, this research highlights the impact of systematic tuning specifically epoch count optimization in enhancing model accuracy for difficult to distinguish disease classes. These findings underscore the urgency of developing high performance, automated disease detection tools to support precision agriculture and sustainable crop health monitoring.
Prediksi Harga Emas Aneka Tambang (Antam) Menggunakan Long Short- Term Memory (LSTM) Ansori, Yulian; Rahman, Arief; Darnis, Febriyanti; Sika Azkia, Czidni; Sholihin, Miftahus
RIGGS: Journal of Artificial Intelligence and Digital Business Vol. 4 No. 4 (2026): November - January
Publisher : Prodi Bisnis Digital Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/riggs.v4i4.3408

Abstract

Emas merupakan salah satu instrumen investasi yang berperan penting dalam menjaga stabilitas ekonomi, khususnya pada periode ketidakpastian global. Pergerakan harga emas sangat dipengaruhi oleh berbagai faktor ekonomi makro seperti nilai tukar, tingkat suku bunga, kebijakan moneter, serta kondisi geopolitik internasional yang dinamis. Kompleksitas hubungan antar variabel tersebut menyebabkan model statistik konvensional kurang optimal dalam memprediksi harga emas secara akurat. Penelitian ini bertujuan untuk mengembangkan model prediksi harga emas PT Aneka Tambang Tbk (ANTAM) menggunakan algoritma Long Short-Term Memory (LSTM), yang merupakan pengembangan dari Recurrent Neural Network (RNN) dan memiliki kemampuan dalam menangkap dependensi jangka panjang pada data deret waktu. Data yang digunakan bersumber dari situs resmi Logam Mulia ANTAM dengan periode pengamatan 4 Januari 2010 hingga 31 Desember 2024 sebanyak 4.545 data harian. Proses penelitian meliputi tahap pengumpulan data, normalisasi, pemisahan data pelatihan dan pengujian, pembangunan model, serta evaluasi menggunakan metrik Root Mean Squared Error (RMSE) dan Mean Absolute Percentage Error (MAPE). Hasil eksperimen menunjukkan bahwa konfigurasi model dengan satu hidden layer, 150 hidden units, dan 150 epoch menghasilkan akurasi sebesar 99,55% dan nilai RMSE sebesar 7.492,998. Temuan ini menunjukkan bahwa algoritma LSTM mampu memberikan performa prediksi yang sangat baik dan berpotensi diterapkan sebagai metode alternatif dalam analisis pergerakan harga komoditas di Indonesia.
Co-Authors Abdul Kholiq Abdul Kholiq Ahmad Fauzi Hendratmoko Alfarisi, Muhammad Nur Fikri AlMuhibbi, Muhammad Rayendra Anam, M. Khairul Ansori, Yulian Arief Rahman Arief Rahman Arshad, Mohamad Syafwan Asmaraningtyas, Kinanthi Trah Asshiddieqie, Rafi Ramadhan Atia Sonda Aulia Ikhsan Azizah, Luluk Nur Azza Abidatin Bettaliyah AZZA ABIDATIN BETTALIYAH Bagus Nur Bakti Aji Bagus Nur Bakti Aji bin MD. Fudzee, Mohd Farhan Cindy Suryanti Darnis, Febriyanti Delano, M. Fabian Reinhard Dinar Mahdalena Leksana 1 Erna Hayati Erna Hayati, Erna Erry Anggraini ERRY ANGGRAINI Farizki, Achmad Nurasel FATHARANI, ATIKA Fatkhul U, M. Miftah Febriyanti Darnis Firdaus, Muhammad Alvin Fudzee, Mohd Farhan Md Gusman, Taufik Hamid, Rahayu A Ismail, Mohd Norasri Izz, Aiz Ahmad Fa’iz Dliya’ul KIKI SEPTARIA Lilik Anifah M. Ghofar Rohman M. Rosidi Zamroni M. ZAKI QOMARUDDIN Mahuda, Isnaini Masruroh MASRUROH Megawati Indriani Mohd Farhan MD Fudzee, Mohd Farhan Mufrody, Moh Adam Mustain Mustain Nafiiyah, Nur Nur Nafi'iyah Nur Nafi’iyah Nurroziqin, M Chabib Nurul Aswa Omar Nurul Ftria ApriLliani Pertiwi, Dinda Dwi Anugrah Prastowo, Diko Pratiwi, Putri Septiani Indah Prisma Nanda Prsatama, Febrian Abie Rahayu A Hamid Retno Wardhani Rofika Arista Sari, Putri Dina Setia Budi, Agus Sika Azkia, Czidni Siti Mujilahwati Sulaiman, Akhmad Nurali Surojuddin, Eko Titin Nurbella Udiansyah, Naufal Arrafi Ulum, M. Miftah Fatkhul Umam, Moch. Zuhrul Vanesta Ikhsana Putri Maulana Wati, Efi Neo Yulian Ansori Zirby, Qonit Zumrotus Shalekhah