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All Journal Jurnal Media Infotama JUTI: Jurnal Ilmiah Teknologi Informasi Jurnal Informatika dan Teknik Elektro Terapan Register: Jurnal Ilmiah Teknologi Sistem Informasi JOIV : International Journal on Informatics Visualization AKSIOLOGIYA : Jurnal Pengabdian Kepada Masyarakat Indonesian Journal of Artificial Intelligence and Data Mining JURNAL TEKNIK INFORMATIKA DAN SISTEM INFORMASI JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) PengabdianMu: Jurnal Ilmiah Pengabdian kepada Masyarakat To Maega: Jurnal Pengabdian Masyarakat Jutisi: Jurnal Ilmiah Teknik Informatika dan Sistem Informasi bit-Tech Jurnal Teknologi Informasi, Komputer, dan Aplikasinya (JTIKA ) JATI (Jurnal Mahasiswa Teknik Informatika) CICES (Cyberpreneurship Innovative and Creative Exact and Social Science) Jurnal Abdi Insani TIN: TERAPAN INFORMATIKA NUSANTARA Journal of Advanced in Information and Industrial Technology (JAIIT) Nusantara Science and Technology Proceedings Kalam Cendekia: Jurnal Ilmiah Kependidikan Journal of Information System and Technology (JOINT) TIERS Information Technology Journal International Journal of Data Science, Engineering, and Analytics (IJDASEA) Decode: Jurnal Pendidikan Teknologi Informasi Jurnal Iqtisaduna COMSERVA: Jurnal Penelitian dan Pengabdian Masyarakat Jurnal Informatika Dan Tekonologi Komputer (JITEK) Jurnal Hasi Penelitian Dan Pengkajian Ilmiah Eksakta - JPPIE Jurnal Informatika Teknologi dan Sains (Jinteks) Concept: Journal of Social Humanities and Education JIKTI : Jurnal Ilmiah Komputer Terapan dan Informasi Mujtama': Jurnal Pengabdian Masyarakat Journal of Artificial Intelligence and Digital Business TAMIKA: Jurnal Tugas Akhir Manajemen Informatika & Komputerisasi Akuntansi ILTEK : Jurnal Teknologi Jurnal Elektronika dan Teknik Informatika Terapan Modem : Jurnal Informatika dan Sains Teknologi Joong-Ki Jurnal Informatika Dan Tekonologi Komputer
Muhammad Muharrom Al Haromainy
Universitas Pembangunan Nasional Veteran Jawa Timur
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KLASIFIKASI PENYAKIT KULIT BERBASIS SUPPORT VECTOR MACHINE DENGAN EKSTRAKSI FITUR ABCD RULE Wibisono, Al Danny Rian; Mandyartha, Eka Prakarsa; Al Haromainy, Muhammad Muharrom
JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Vol 10, No 1 (2025)
Publisher : STKIP PGRI Tulungagung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29100/jipi.v10i1.6039

Abstract

Penyakit kulit merupakan masalah kesehatan yang signifikan, gejala dari penyakit ini berupa gatal, nyeri, mati rasa, dan kemerahan. Penyakit ini dapat disebabkan oleh beberapa faktor seperti virus, jamur, dan mikroorganisme. Menurut data Dinas Kesehatan Surabaya tahun 2019, prevalensi penyakit kulit dan jaringan subkutan mencapai 4,53%, menjadikannya penyakit terbanyak keenam yang dialami masyarakat. Oleh sebab itu, pada penelitian ini diusulkan sebuah penelitian mengenai klasifikasi penyakit kulit menggunakan Support Vector Machine melalui analisis fitur ABCD Rule. Pada penelitian ini akan dilakukan labeling pada 5 kelas penyakit kulit yang akan digunakan sebagai data latih dan data uji melalui 7 tahapan utama yakni Pengumpulan Dataset Citra Penyakit Kulit, Pre-processing Inpaint Talea, Pre-processing Gaussian Blur dan Normalisasi Mask, Segmentasi Thresholding Otsu Bitwise, Restorasi Kontur, Ekstraksi Fitur ABCD Rule, dan klasifikasi menggunakan Support Vector Machine (SVM). Sebanyak 4 skenario pengujian dilakukan untuk menemukan model terbaik, dimana skenario pengujian melibatkan pengaturan pembagian data yang berbeda, kernel berbeda, dan parameter yang berbeda pada model Support Vector Machine (SVM). Melalui skenario tersebut didapatkan hasil terbaik, yaitu Akurasi sebesar 86,42%, Spesifisitas sebesar 96,60%, dan Sensitivitas sebesar 86,42%. Hal ini menunjukkan bahwa metode yang diusulkan memiliki kinerja yang cukup baik dalam mengklasifikasikan jenis penyakit kulit. Penelitian ini tidak hanya berpotensi dalam meningkatkan diagnosis penyakit kulit secara efisien, tetapi juga mendorong pengembangan sistem deteksi berbasis teknologi untuk mendukung layanan kesehatan kulit yang lebih terjangkau dan andal.
Optimizing Gaussian Mixture Model Using Principal Component Analysis for Welfare Clustering Wahyu Gunawan, Rafif Ilafi; Al Haromainy, Muhammad Muharrom; Junaidi, Achmad
bit-Tech Vol. 8 No. 3 (2026): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i3.3310

Abstract

Welfare inequality among regions remains a fundamental challenge in achieving balanced development across East Java Province. The complexity of social, economic, and development indicators often obscures the true patterns of regional welfare. To address this issue, this study proposes a more efficient analytical approach by integrating Principal Component Analysis (PCA) and the Gaussian Mixture Model (GMM) to cluster regions based on welfare levels. The dataset, obtained from the Central Bureau of Statistics (BPS) of East Java for the 2010–2024 period, includes diverse social and economic indicators. PCA was employed to reduce dimensionality and eliminate variable correlations, preserving the essential information within the data. The resulting principal components were then analyzed using GMM to uncover welfare clustering patterns. Based on the evaluation using the Bayesian Information Criterion (BIC) and silhouette score, the optimal configuration was achieved with two clusters, a tolerance of 1e-2, a maximum iteration of 200, and a silhouette score of 0.3403. The first cluster represented regions with higher welfare conditions, while the second indicated relatively lower welfare. These findings demonstrate that the PCA–GMM integration not only improves clustering accuracy but also enhances interpretability of welfare distribution across regions. Future studies may combine PCA with non-linear dimensionality reduction techniques such as Uniform Manifold Approximation and Projection (UMAP) to preserve local structures within complex datasets. Such integration is expected to reveal subtler and more dynamic welfare patterns, offering deeper insights into regional development disparities.
Comparative Analysis of LSTM and GRU Algorithms for Inflation Rate Forecasting Ardiyansyah, Moh. Angga; Al Haromainy, Muhammad Muharrom; Junaidi, Achmad
bit-Tech Vol. 8 No. 3 (2026): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i3.3370

Abstract

Inflation is a critical economic indicator that directly affects price stability, purchasing power, and the formulation of fiscal and monetary policies. In East Java, inflation has demonstrated considerable year-to-year volatility, creating significant challenges for policymakers in maintaining regional economic stability. This situation highlights the need for forecasting models that are both accurate and capable of adapting to complex economic data patterns. This study presents a comparative analysis of two deep learning algorithms Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) for forecasting year-on-year (YoY) inflation in East Java using data from January 2005 to December 2024. The dataset was processed using Min–Max normalization and a 12-month sliding window to capture long-term dependencies and seasonal variations. Model performance was evaluated using RMSE, MAE, and MAPE. The findings demonstrate that no single model performs best across all metrics. The LSTM4 model with a [128,128] architecture achieved the lowest MAE and MAPE values, indicating superior average predictive accuracy and stronger capability in learning complex long-term inflation patterns. In contrast, the GRU1 [64,64] model produced the lowest RMSE and the shortest training time, highlighting its efficiency in minimizing extreme prediction errors and reducing computational cost. These results offer valuable insights for policymakers in East Java: LSTM is more suitable for applications requiring high prediction accuracy, whereas GRU is preferable for real-time or resource-efficient forecasting systems, especially in fast-changing economic environments.
Analisis Perbandingan Deteksi Penyakit Daun Jagung Menggunakan YOLO dan CNN Rifqi, Mohammad Habim Hazidan; Haromainy, Muhammad Muharrom Al; Nurlaili, Afina Lina
bit-Tech Vol. 8 No. 3 (2026): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i3.3392

Abstract

This study compares the performance of two deep learning methods, You Only Look Once version 8 (YOLOv8) and the Convolutional Neural Network (CNN) EfficientNetB0, in detecting and classifying maize leaf diseases. The background of this research stems from the importance of early plant disease identification to support food security, as well as the limitations of manual inspection methods, which are slow, subjective, and inefficient. The study combines primary and secondary data, totaling 2,000 images that underwent undersampling, augmentation, resizing, and bounding box annotation for YOLO training needs. Both models were trained on the same dataset with an 80% training and 20% testing split. YOLOv8n was trained using a transfer learning approach for 30 epochs, while the CNN was trained using EfficientNetB0 with similar training parameters. The results show that YOLOv8 achieved high detection performance with an mAP@0.5 of 0.985 and the highest class accuracy in the Healthy category (0.99). Meanwhile, the CNN demonstrated more stable classification performance, achieving the highest accuracy in the Grey Leaf Spot class (0.99) and a validation accuracy of 0.96. The comparison indicates that YOLO excels in object detection and disease localization in field images, whereas the CNN is more consistent in classifying segmented leaf images. These findings provide practical implications for real world deployment: YOLOv8 is suitable for real time detection in field conditions, including potential integration into mobile based early warning systems for farmers, while EfficientNetB0 is more appropriate for offline or laboratory based classification of static leaf samples.
Implementasi CNN Untuk Klasfikasi Emosi Dalam Lagu Berdasarkan Fitur Audio Pakpahan, Fredrik Sahalatua; Haromainy, M. Muharrom Al; Mulyo, Budi Mukhamad
bit-Tech Vol. 8 No. 3 (2026): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i3.3438

Abstract

Music is a powerful art form for conveying and evoking emotions; however, the vast volume of digital music data makes manual emotion categorization difficult. This study aims to implement a Convolutional Neural Network (CNN) to classify emotions in instrumental songs based on audio features. The dataset used is the Database for Emotional Analysis of Music (DEAM), containing 1,802 songs with valence and arousal annotations, which is divided with a 70:15:15 ratio for training, validation, and testing. The feature extraction methods applied include Mel-Frequency Cepstral Coefficients (MFCC) with variations of 13, 24, and 30 coefficients, and Mel-spectrograms with variations of 128, 256, and 512 bins. Data is processed through pre-emphasis and framing stages before being input into a CNN architecture with four convolutional blocks. Evaluation was conducted using 4-quadrant classification scenarios and a simplification into 2 quadrants. The results showed that in the 4-quadrant classification, the best model was achieved using MFCC with 30 coefficients with an accuracy of 66%, but model performance was hindered by extreme minority class imbalance. Conversely, simplifying the emotion space into 2 quadrants (valence or arousal) significantly improved accuracy to 77%. This study concludes that while increasing feature resolution has a minor impact, simplifying emotion dimensions proves more effective in addressing complexity and data imbalance in music emotion classification.
Design of Thesis Topic Recommendation System Using TF-IDF and Cosine Similarity Arrisalah, Muhammad Baihaqi; Haromainy, Muhammad Muharrom Al; Junaidi, Achmad
bit-Tech Vol. 8 No. 3 (2026): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i3.3579

Abstract

Selecting a thesis topic is a critical stage in a student’s academic journey and frequently poses substantial cognitive and procedural challenges. This study reports the design and implementation of the Computer Science Thesis Recommendation System (SRSIK Hub), a web-based decision-support platform aimed at improving the efficiency and accuracy of thesis topic selection. The primary novelty of this research lies in the systematic integration of Term Frequency–Inverse Document Frequency (TF-IDF) and Cosine Similarity within a large-scale academic corpus to model fine-grained semantic relevance between student interests and prior thesis documents, enabling more precise and transparent recommendations than conventional keyword-based searches. The system adopts a content-based filtering approach and processes approximately 4,000 thesis records collected from multiple university repositories. Textual data are preprocessed and transformed using TF-IDF vectorization, while Cosine Similarity is employed to rank candidate topics according to relevance. System effectiveness was evaluated using the WebUse Framework involving 75 student respondents. The evaluation yielded an overall score of 4.44 out of 5, indicating high usability, strong information quality, and reliable system functionality. This performance score demonstrates that the proposed recommendation model is not only technically sound but also practically applicable in real academic settings, where it can significantly reduce topic selection time and uncertainty for students. The results confirm that SRSIK Hub effectively supports students in identifying research topics aligned with their academic interests and competencies. Beyond local deployment, the system is transferable to other institutions for scalable thesis recommendation support.
Prediksi Gangguan Kesehatan Mental pada Kalangan Mahasiswa Menggunakan Metode Pseudo-Labeling dan Algoritma Regresi Logistik Anggraini Puspita Sari; Dwi Arman Prasetya; Firza Prima Aditiawan; Muhammad Muharrom Al Haromainy
TAMIKA: Jurnal Tugas Akhir Manajemen Informatika & Komputerisasi Akuntansi Vol 4 No 2(SEMNASTIK) (2024): TAMIKA: Jurnal Tugas Akhir Manajemen Informatika & Komputerisasi Akunt
Publisher : Universitas Methodist Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46880/tamika.Vol4No2(SEMNASTIK).pp40-48

Abstract

Mental illness is a health condition that alters a person's thoughts, feelings, or behaviors, leading to distress and difficulty in maintaining a normal life. Mental health issues should not be taken lightly due to the challenges associated with diagnosis. Many students tend to experience mental health problems at various stages of their education, from diploma programs to doctoral studies. This situation becomes more critical as students approach the end of their studies and anticipate future prospects. This article explores the mental health status of students through symptoms, using logistic regression methods for prediction based on the dataset used. In this study, two types of data are employed: labeled dataset and unlabeled dataset, which are combined to create a semi-supervised learning approach. Labeled dataset is classified using a logistic regression algorithm, while unlabeled dataset employs the pseudo-labeling method. The analysis and modeling of the dataset indicate that the comparison between labeled and unlabeled dataset can significantly affect accuracy and processing time. Furthermore, the use of the pseudo-labeling method with the logistic regression algorithm is well-suited for the mental health case study, achieving an accuracy of 98% with a labeled to unlabeled dataset ratio of 1:2.
The Impact of Small Group Interaction Techniques on Student Achievement in Reading Comprehension Purnomo, Ryan; Rahmawan, Ganal Arief; Septianto, Tri; Al Haromainy, Muhammad Muharrom; Almanfakulti, Istian Kriya; Boyas, Jeziano Rizkita
Kalam Cendekia: Jurnal Ilmiah Kependidikan Vol 12, No 1 (2024): Kalam Cendekia: Jurnal Ilmiah Kependidikan
Publisher : Universitas Sebelas Maret

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20961/jkc.v12i1.84526

Abstract

Penelitian ini bertujuan untuk mengeksplorasi efektivitas pengajaran membaca dalam kurikulum bahasa Inggris di Universitas Nahdlatul Ulama Sidoarjo dengan menggunakan dua metode pengajaran yang berbeda: interaksi kelompok kecil dan metode konvensional. Pendekatan penelitian ini bersifat kuantitatif dengan fokus pada perbandingan kemampuan pemahaman membaca antara kelompok eksperimen dan kelompok kontrol. Studi ini menyelidiki apakah siswa yang diajarkan melalui interaksi kelompok kecil menunjukkan kemampuan membaca yang lebih unggul dibandingkan dengan mereka yang diajarkan menggunakan metode konvensional. Penelitian ini menggunakan pendekatan kuantitatif, mengungkapkan perbedaan yang signifikan dalam kemampuan pemahaman membaca secara keseluruhan. Nilai uji-t yang dihitung untuk pemahaman membaca umum adalah 7,85, melebihi nilai kritis p<.05 dengan uji satu sisi sebesar 1.671 (d.f.= 60). Berdasarkan analisis ini, dapat disimpulkan bahwa siswa di kelompok eksperimen menunjukkan keahlian yang lebih besar dibandingkan dengan mereka di kelompok kontrol, menunjukkan bahwa teknik interaksi kelompok kecil lebih efektif daripada metode konvensional di Universitas Nahdlatul Ulama Sidoarjo.
Deepening Understanding of Specific Issues through Structured Academic Controversy: A Perspective of Cooperative Learning Purnomo, Ryan; Fitriyah, Nur Nafisatul; Suprapti, Suprapti; Al Haromainy, Muhammad Muharrom
Kalam Cendekia: Jurnal Ilmiah Kependidikan Vol 11, No 3 (2023): Kalam Cendekia: Jurnal Ilmiah Kependidikan
Publisher : Universitas Sebelas Maret

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20961/jkc.v11i3.79550

Abstract

Penelitian ini bertujuan untuk memperkenalkan dan menekankan pemanfaatan kontroversi akademik terstruktur sebagai model teknik pembelajaran kooperatif bagi mahasiswa universitas. Meskipun pentingnya berpikir kritis dan pembelajaran kooperatif dalam pendidikan tinggi semakin meningkat, kurangnya teknik yang efektif menghambat perkembangan keterampilan penting ini. Tujuan utama dari penelitian ini adalah untuk menunjukkan efektivitas kontroversi akademik terstruktur dalam meningkatkan keterampilan berpikir kritis dan mempromosikan pembelajaran kolaboratif di kalangan mahasiswa universitas yang mempelajari bahasa Inggris sebagai bahasa asing (EFL). Melalui tinjauan komprehensif terhadap literatur yang ada dan studi kasus, penelitian ini menyoroti implementasi sukses kontroversi akademik terstruktur dalam beragam pengaturan kelas EFL, menampilkan adaptabilitas dan efektivitasnya dalam memfasilitasi pembelajaran kooperatif dan keterampilan berpikir kritis di antara mahasiswa.Temuan menunjukkan peningkatan yang signifikan dalam kemampuan mahasiswa untuk menganalisis secara kritis isu-isu kompleks, mengembangkan pandangan yang nuansat, dan berinteraksi secara konstruktif dengan sudut pandang yang berlawanan, mengindikasikan potensi kontroversi akademik terstruktur dalam menciptakan lingkungan pembelajaran yang kokoh. Sebagai kesimpulan, integrasi sistematis kontroversi akademik terstruktur dalam lingkungan pembelajaran EFL dapat secara signifikan berkontribusi pada pengembangan keterampilan berpikir kritis dan mempromosikan suasana pendidikan yang kolaboratif dan intelektual yang merangsang.
Co-Authors Abadi, Luthfiyana Mahrurin Abdillah, Ikhwan Abdul Rezha Efrat Najaf Achmad Andrian Maulana Achmad Junaidi Achmad Rozy Priambodo Afina Lina Nurlaili Afina Lina Nurlaili Afina Lina Nurlaili Agung Mustika Rizki, Agung Mustika Agus Wibowo Agus Zainal Arifin Ahmad Saikhu Akbar, Fawwaz Ali Al Fatih, Abdullah Alghiffary, Rizqi Almanfakulti, Istian Kriya Alya Izzah Zalfa Rihadah Ramadhani Nirwana Putri Ananda Ayu Puspitaningrum Andreas Nugroho Sihananto Angga Lisdiyanto Anggraini Puspita Sari Anita Puspitasari Annisa Dwi Puspitarini Anugerah, Rico Putra Ardiyansyah, Moh. Angga Arrisalah, Muhammad Baihaqi ASHARI, FAISAL Avi Sunani Aviolla Terza Damaliana Azira, Volem Alvaro Azira Basuki Rahmat Masdi Siduppa Bima Arya Kurniawan Boyas, Jeziano Rizkita Budi Nugroho Budi Nugroho Chairil, Augustin Mustika Chastine Fatichah Christianty, Theressa Marry Darmawan, Marcellinus Aditya Vitro Dwi Arman Prasetya Dwi Arman Prasetya Dwi Arman Prasetya Dwi Sutrisno, Rahmat Edi Sugiyanto Eka Prakarsa Mandyartha Eva Yulia Puspaningrum Fania Imelda Safitri Faris Syaifulloh Farkhan Fauzan Akbari, Muhamad Fauzi, Zaky Ahmad Ferry Trilaksana Putra Fetty Tri Anggraeny Firza Prima Aditiawan Fitrani, Laqma Dica Fitriyah, Nur Nafisatul Gusti Eka Yuliastuti Hajjar, Debrina Octrisya Hardiansyah, In Naka Malik Hidra Amnur I Wayan Alston Argodi Kartini Kartini Kevin Iansyah Kurnia, Lusi Kusuma Wardani, Amalia Dwi Lailatul Musyaffaah Lina Nurlaili, Afina Lintang Putri Permatasari Lisdiyanto, Angga Lusian Nandang Arjamulia Maulana Herza, Fakhri Maulana, Hendra Maulana, Vieri Arief Mohammad Setyo Wardono Muhammad Albert Nur Agathon Muhammad Daffa Arifin Muhammad Izdihar Alwin Mulyo, Budi Mukhamad Muzdalifah, Nayani Alya Aquila Nia Dwi Puspitasari Nurlaili, Afina Lina Nurrahman, Sintya Fadillah Oktaviana, Dinda Friska Pakpahan, Fredrik Sahalatua Panjaitan, Tompo Paramitha, Clara Diva Permatasari, Reisa Pratama Wirya Atmaja Prinafsika Purnomo, Ryan Putra, Chrystia Aji Putra, Gredy Christian Hendrawan Raden Kokoh Haryo Putro Rafie Ishaq Maulana Rahmawan, Ganal Arief Retno Mumpuni Reza, Reno Alfa Rifqi, Mohammad Habim Hazidan Riza Satria Putra Rizka Fadhillah, Irnanda Ryan Purnomo Samodera, Bayu Sari, Rizky Buana Satrio, Deva Dwi Setyawan, Dimas Ari Shalehuddin Albawani, Raden Siregar, Talitha Aurora Nadenggan Sujayanti, Forentina Kerti Pratiwi Suprapti Suprapti Taufiqqurrahman, Husain Tri Septianto Trimono, Trimono Triyana, Dimas Volem Alvaro Azira Azira Wahyu Eko Pujianto Wahyu Fahrul Ridho Wahyu Gunawan, Rafif Ilafi Wahyu Syaifullah JS Waluya, Onny Kartika Waskito, Achmad Derajat Wibisono, Al Danny Rian Widowati, Elok Winarti ., Winarti Yisti Vita Via