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All Journal IJCCS (Indonesian Journal of Computing and Cybernetics Systems) TEKNIK INFORMATIKA Seminar Nasional Aplikasi Teknologi Informasi (SNATI) Semantik Techno.Com: Jurnal Teknologi Informasi Jurnal Teknologi Informasi dan Ilmu Komputer Proceeding of the Electrical Engineering Computer Science and Informatics Fountain of Informatics Journal Jurnal ELTIKOM : Jurnal Teknik Elektro, Teknologi Informasi dan Komputer Sinkron : Jurnal dan Penelitian Teknik Informatika Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) RABIT: Jurnal Teknologi dan Sistem Informasi Univrab Indonesian Journal on Software Engineering (IJSE) Faktor Exacta Jukung (Jurnal Teknik Lingkungan) CogITo Smart Journal Indonesian Journal of Artificial Intelligence and Data Mining INOVTEK Polbeng - Seri Informatika JRMSI - Jurnal Riset Manajemen Sains Indonesia KACANEGARA Jurnal Pengabdian pada Masyarakat JSiI (Jurnal Sistem Informasi) Jurnal Nasional Pendidikan Teknik Informatika (JANAPATI) Informatik : Jurnal Ilmu Komputer Jurnal Riset Informatika JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) METIK JURNAL Scientific Journal of Informatics Jifosi Idealis : Indonesia Journal Information System SKANIKA: Sistem Komputer dan Teknik Informatika Jurnal Teknik Informatika (JUTIF) Jurnal PkM (Pengabdian kepada Masyarakat) Kresna: Jurnal Riset dan Pengabdian Masyarakat Bit (Fakultas Teknologi Informasi Universitas Budi Luhur) Jurnal Algoritma Jurnal Ticom: Technology of Information and Communication Journal of Social And Economics Research Journal Of Communication Education Telematika MKOM Jurnal INFOTEL Jurnal Ticom: Technology of Information and Communication CSRID journal of social and economic research JuTISI (Jurnal Teknik Informatika dan Sistem Informasi)
Achmad Solichin
Universitas Budi Luhur
Aerospace Engineering Agriculture, Biological Sciences & Forestry Arts Humanities Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Social Sciences Other

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SVM Optimization with Grid Search Cross Validation for Improving Accuracy of Schizophrenia Classification Based on EEG Signal Desiawan, Masdar; Solichin, Achmad
JURNAL TEKNIK INFORMATIKA Vol. 17 No. 1: 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.v17i1.37422

Abstract

The advantage of the Support Vector Machine (SVM) is that it can solve classification and regression problems both linearly and non-linearly. SVM also has high accuracy and a relatively low error rate. However, SVM also has weaknesses, namely the difficulty of determining optimal parameter values, even though setting exact parameter values affects the accuracy of SVM classification. Therefore, to overcome the weaknesses of SVM, optimizing and finding optimal parameter values is necessary. The aim of this research is SVM optimization to find optimal parameter values using the Grid Search Cross-Validation method to increase accuracy in schizophrenia classification. Experiments show that optimization parameters always find a nearly optimal combination of parameters within a specific range. The results of this study show that the level of accuracy obtained by SVM with the grid search cross-validation method in the schizophrenia classification increased by 9.5% with the best parameters, namely C = 1000, gamma = scale, and kernel = RBF, the best parameters were applied to the SVM algorithm and obtained an accuracy of 99.75%, previously without optimizing the accuracy reached 90.25%. The optimal parameters of the SVM obtained by the grid search cross-validation method with a high degree of accuracy can be used as a model to overcome the classification of schizophrenia.
Pencarian Abstrak Tugas Akhir Mahasiswa Berdasarkan Tingkat Kemiripan Menggunakan Algoritma Winnowing dan Jaccard Similarity pada Universitas Budi Luhur Desena, Wahyu; Solichin, Achmad
Informatik : Jurnal Ilmu Komputer Vol 17 No 2 (2021): Agustus 2021
Publisher : Fakultas Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52958/iftk.v17i2.3628

Abstract

Dokumen skripsi merupakan dokumen yang merepresentasikan penelitian yang dilakukan oleh mahasiswa jenjang strata satu. Untuk menghasilkan skripsi yang baik dibutuhkan studi literatur untuk mendukung penelitian tersebut. Pada Universitas Budi Luhur, sudah tersedia sistem pencarian literatur dalam bentuk dokumen skripsi. Namun demikian, pencarian data masih terbatas berdasarkan kesamaan judul dengan kata kunci yang diberikan. Hal tersebut mengakibatkan hasil pencarian tidak terlalu akurat. Oleh karena itu, pada penelitian ini diusulkan sistem yang mampu menyajikan hasil pencarian berdasarkan tingkat kemiripan dokumen. Metode yang digunakan adalah algoritma Winnowing berbasis N-Gram dengan perhitungan kemiripan metode Jaccard Similarity. Berdasarkan hasil pengujian, nilai k-gram dan w-gram mempengaruhi persentase kemiripan dokumen yang mana nilai terbaik adalah k-gram=3 dan w-gram=4. Prototipe sistem yang dihasilkan dapat menyajikan hasil pencarian beserta nilai kemiripan dokumen abstrak tugas akhir yang diinputkan dengan dokumen yang tersimpan di repository.
PENERAPAN ALGORITMA GENETIKA UNTUK PENJADWALAN MATA PELAJARAN PADA PESANTREN AL-QUR'AN DAARUS SHOFWAH Abdurrohim Musthofa; Solichin, Achmad
JIFOSI Vol. 6 No. 1 (2025): Smart Systems and Data-Driven Approaches in Business and Technology
Publisher : UPN "Veteran" Jawa Timur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33005/jifosi.v6i1.467

Abstract

Penjadwalan mata pelajaran merupakan aspek krusial dalam sistem pendidikan, termasuk di lembaga pendidikan Islam seperti pesantren. Pesantren Al-Qur'an Daarus Shofwah menghadapi tantangan signifikan dalam menyusun jadwal pelajaran yang efektif dan efisien. Tantangan tersebut mencakup keterbatasan ketersediaan guru, ruang kelas, serta beragam kebutuhan santri. Selama ini, penjadwalan di pesantren ini dilakukan secara manual, yang sering kali tidak efisien dan memakan waktu. Penelitian ini bertujuan untuk mengoptimalkan penjadwalan mata pelajaran di Pesantren Al-Qur'an Daarus Shofwah dengan menggunakan algoritma genetika. Algoritma genetika merupakan metode komputasi yang meniru mekanisme evolusi alami, seperti seleksi, crossover, dan mutasi, untuk menemukan solusi optimal pada permasalahan kompleks. Dalam konteks penjadwalan ini, algoritma genetika digunakan untuk menyusun jadwal yang memenuhi berbagai kriteria dan batasan, seperti ketersediaan waktu guru, jumlah ruang kelas, serta alokasi waktu yang optimal bagi para santri. Hasil penelitian menunjukkan bahwa penerapan algoritma genetika berhasil menghasilkan jadwal pelajaran yang lebih efisien dan sesuai dengan kebutuhan dibandingkan dengan metode manual. Algoritma ini mampu memproses berbagai variabel dan menghasilkan solusi yang optimal dalam waktu yang lebih singkat. Selain itu, jadwal yang dihasilkan juga lebih fleksibel dan dapat disesuaikan dengan perubahan kondisi yang dinamis di pesantren. Diharapkan, penerapan algoritma genetika dalam penjadwalan ini tidak hanya meningkatkan efisiensi operasional, tetapi juga kualitas pendidikan di Pesantren Al-Qur'an Daarus Shofwah, sehingga dapat memberikan dampak positif yang signifikan bagi proses belajar mengajar di pesantren tersebut.
ANALISIS KLASTER PEMBELAJARAN DARING MAHASISWA BERDASARKAN AKTIFITAS LOG DATA PENGGUNA LEARNING MANAGEMENT SYSTEM (LMS) MENGGUNAKAN METODE K-MEANS DAN K-MEDOIDS Pratama, Andika; Ikhsan, Rifqi Dainur; Solichin, Achmad
JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Vol 10, No 4 (2025)
Publisher : STKIP PGRI Tulungagung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29100/jipi.v10i4.9143

Abstract

Pembelajaran daring telah menjadi komponen penting dalam pendidikan tinggi, terutama dalam menghadapi tantangan pembelajaran jarak jauh. Learning Management System (LMS) menyediakan platform yang memungkinkan pemantauan aktivitas mahasiswa secara rinci. Namun, besarnya volume data log aktivitas pengguna di LMS menimbulkan tantangan dalam menganalisis pola pembelajaran yang relevan. Penelitian ini bertujuan untuk mengelompokkan mahasiswa berdasarkan aktivitas log mereka pada LMS menggunakan metode klasterisasi K-Means dan K-Medoids. Proses analisis diawali dengan tahap persiapan data, termasuk pembersihan, reduksi, transformasi, dan ekstraksi fitur utama, seperti frekuensi login, jumlah akses materi, partisipasi kuis, dan pengumpulan tugas. Evaluasi klaster optimal dilakukan dengan metode Elbow Method, Silhouette Index, dan Calinski-Harabasz Index, yang menentukan jumlah klaster terbaik pada K=3. Untuk menilai kualitas klasterisasi, digunakan metrik Davies-Bouldin Index (DBI). Hasil evaluasi menunjukkan bahwa algoritma K-Means memiliki nilai DBI lebih rendah (0,261) dibandingkan K-Medoids (0,340), yang menunjukkan bahwa K-Means lebih optimal dalam mengelompokkan aktivitas mahasiswa. Analisis lebih lanjut menunjukkan adanya korelasi positif antara aktivitas mahasiswa di LMS dengan performa akademik mereka. Mahasiswa dengan aktivitas tinggi memiliki rata-rata nilai akademik lebih baik (84,71), sedangkan mahasiswa dengan aktivitas rendah memiliki nilai rata-rata lebih rendah (55,80).
Analisis Komparatif Akurasi Deteksi Teks Dokumen Keuangan Menggunakan CTPN dan EAST Jody; Achmad Solichin
CSRID (Computer Science Research and Its Development Journal) Vol. 18 No. 2 (2026): Juni 2026
Publisher : LPPM Universitas Potensi Utama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22303/csrid-.18.2.2026.274-286

Abstract

Automation of data extraction in financial documents is an urgent necessity for MYO Adventures to mitigate the risks of manual entry errors and operational inefficiencies. This study aims to evaluate the performance of Deep Computer Vision modules within an automated validation system through a comparative analysis between Connectionist Text Proposal Network (CTPN) and Efficient and Accurate Scene Text Detector (EAST) algorithms. The research methodology employs a quantitative experimental approach, testing both models against invoice and receipt datasets characterized by physical distortions and dense tabular layouts. Evaluation focuses on inference time metrics and text localization effectiveness prior to the Optical Character Recognition (OCR) stage. The results reveal a significant performance disparity, where the EAST algorithm recorded an average detection time of 565 ms, substantially more efficient than CTPN which required an extreme 12,046 ms. In terms of coverage accuracy, EAST demonstrated high robustness by successfully recognizing 74 valid text items, in contrast to CTPN which yielded only 14 readable items. The poor performance of CTPN is analyzed as a consequence of the limitations of its vertical anchor mechanism and Recurrent Neural Network (RNN) in handling the dense spacing variations of invoice tables, whereas the Fully Convolutional Network (FCN) architecture of EAST proved to be more adaptive. In conclusion, anchor-free methods like EAST possess superior reliability and computational efficiency, making them the most viable solution for implementing real-time financial validation systems on MYO Adventures' web-based platform.
Pengamanan M-Commerce Menggunakan One Time Password Metode Pseudo Random Number Generator (PRNG) Muhammad Fahrizal; Achmad Solichin
Rabit : Jurnal Teknologi dan Sistem Informasi Univrab Vol 5 No 2 (2020): Juli
Publisher : LPPM Universitas Abdurrab

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (991.897 KB) | DOI: 10.36341/rabit.v5i2.1363

Abstract

Mobile commerce or m-commerce is an electronic trading system (e-commerce) that uses mobile equipment such as mobile phones, smart phones, PDAs, and notebooks. With the growth of smartphone users throughout the world, many electronic commerce business owners also provide m-commerce applications to make it easier for their customers to make transactions. In addition to providing convenience for users, m-commerce application providers must be able to ensure that customers can transact safely. Security risks are one of the major obstacles in the development of electronic commerce systems. Therefore, this research applies the method of securing m-commerce applications using one time password (OTP) generated by the Pseudo Random Number Generator (PRNG) method. This study also modified the PRNG algorithm by doing three bit shifting processes and adding encryption algorithms. The test results show that the system can generate an OTP that is always unique for each transaction. The results of this study are useful for m-commerce application developers to secure their applications.
ANALISIS PENGELOMPOKAN POLA PENJUALAN PRODUK UMKM MENGGUNAKAN ALGORITMA K-MEANS Putri Diana; Achmad Solichin
Rabit : Jurnal Teknologi dan Sistem Informasi Univrab Vol 11 No 1 (2026): Januari
Publisher : LPPM Universitas Abdurrab

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36341/rabit.v11i1.7325

Abstract

Developments in machine learning technology offer significant opportunities for Micro, Small, and Medium Enterprises (MSMEs), particularly those operating in the Warung Madura sector, to analyze sales patterns in greater depth through data processing. This article aims to evaluate the sales patterns of MSME Warung Madura products by utilizing the K-Means Clustering algorithm. The data used in this study include several key parameters, namely product price, sales volume, and profit, taken from sales transaction records at one MSME Warung Madura. The analysis was carried out through a process that includes pre-processing, information normalization, and the application of the K-Means algorithm with a value of k = 3 to group products based on similarities in their sales characteristics. The findings of this study indicate the formation of three product categories, namely (1) products with affordable prices and small margins, (2) premium products with high sales levels and large profits, and (3) products with stable sales performance. The results of this clustering provide a clear product map, allowing business owners to allocate resources (stock, promotions) more strategically based on cluster characteristics. Thus, the application of machine learning using the K-Means algorithm can provide valuable insights to support the digitalization process and increase efficiency in managing the Warung Madura MSME business.
Early Detection of Hepatitis Disease Using Machine Learning Algorithms Maya Gian Sister; Yulia Nita; Achmad Solichin
Indonesian Journal of Artificial Intelligence and Data Mining Vol. 8 No. 3 (2025): November 2025
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

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

Abstract

Hepatitis is an inflammation of the liver caused by viral infections, autoimmune disorders, or exposure to toxic substances. Hepatitis B and C are major public health concerns because they may progress to cirrhosis or liver cancer. In Indonesia, the transmission rate remains high, primarily through blood contact, unsterile needles, transfusions, and maternal delivery. Limited public awareness, coupled with the often asymptomatic nature of hepatitis, leads to delayed detection, which increases the risk of severe complications and mortality. Therefore, early detection is crucial to minimizing the disease burden.This study proposes a risk prediction model for hepatitis using non-laboratory clinical data and machine learning methods. Eight classification algorithms were compared, Naïve Bayes, K-Nearest Neighbor (K-NN), Random Forest, Support Vector Machine (SVM), Decision Tree, AdaBoost, XGBoost, CatBoost, and LightGBM. Model performance was evaluated through K-fold cross-validation using accuracy, precision, recall, F1-score, and AUC. The results show that the SVM with a linear kernel achieved the highest performance, with 87% accuracy and balanced F1-scores across all classes. The model successfully classified four categories, Acute Hepatitis, Chronic Hepatitis, Liver Abscess, and Parasitic/Viral Infections. These findings highlight the potential of machine learning to improve early detection of hepatitis effectively and efficiently.
Early Detection of Depression Levels Among Gen-Z Using TikTok Data and Extra Trees Ensemble Classifier Solichin, Achmad; Zulqan, Helmi; Painem, Painem; Pradiptha, Anindya Putri
Jurnal Teknik Informatika (Jutif) Vol. 7 No. 2 (2026): JUTIF Volume 7, Number 2, April 2026
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2026.7.2.5357

Abstract

Mental health disorders, particularly depression, have become an increasingly critical issue, especially among young people aged 15–29 years. Social stigma and limited awareness often hinder early detection and intervention. In the digital era, social media platforms such as TikTok provide opportunities to observe users’ behavioral patterns that may reflect their psychological conditions. This study proposes an early depression detection model based on TikTok social media data using an ensemble machine learning approach, namely the Extra Trees classifier. Data were collected from 263 undergraduate students through an online survey combined with automated crawling of respondents’ TikTok accounts. Depression levels were labeled using the Patient Health Questionnaire-9 (PHQ-9) and categorized into four classes: none, mild, moderate, and severe. After data selection, feature extraction, and class balancing using SMOTE, the final dataset consisted of 600 instances with 24 features, including demographic attributes, TikTok activity metrics, and social network analysis features. Experimental results indicate that the Extra Trees classifier achieved the highest performance, with an accuracy, precision, recall, and F1-score of 91%, outperforming Decision Tree, Random Forest, XGBoost, LightGBM, and CatBoost. The model demonstrated stable performance across all depression levels and efficient prediction time suitable for near real-time web-based applications. These findings confirm that integrating behavioral and network-based social media features with validated psychological assessments can support effective early depression screening. This research contributes to mental health informatics and social media analytics within the field of computer science by demonstrating the effectiveness of ensemble learning for depression detection using TikTok-based digital behavioral data.
Prediksi Tingkat Kesepian Berdasarkan Profil Media Sosial Menggunakan Algoritma Random Forest Asep Lukman Arip Hidayat; Achmad Solichin
Jurnal Sistem Informasi Vol. 13 No. 1 (2026)
Publisher : Universitas Serang Raya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30656/10rg3t91

Abstract

Pada era digital yang semakin berkembang, media sosial telah menjadi bagian penting yang tidak terpisahkan dari kehidupan manusia modern, terutama bagi generasi muda, termasuk Generasi Z di SMK Tunas Media yang lahir pada awal abad ke-21 dan menjadi generasi pertama yang tumbuh dengan akses internet, teknologi dan media sosial. Beberapa penelitian terkini mengungkapkan bahwa meskipun Generasi Z tumbuh dalam era teknologi yang sangat terhubung, mereka ternyata memiliki gejala tingkat kesepian yang sangat tinggi bahkan melampaui seluruh generasi sebelumnya, salah satu penyebabnya adalah penggunaan media sosial yang berlebihan, sehingga mereka lebih mengutamakan komunikasi digital dibandingkan interaksi secara langsung. Sekitar 73% dari mereka mengatakan bahwa mereka merasa terisolasi. Hal ini tentunya berdampak pada kesehatan mental mereka, 91% melaporkan mengalami stres fisik atau emosional, dan 68% mengatakan bahwa mereka merasa khawatir tentang masa depan. Oleh karena itu, sangatlah penting untuk melakukan deteksi dini pada siswa-siswi SMK Tunas Media yang berisiko mengalami kesepian. Teknologi pembelajaran mesin Random Forest, dapat digunakan untuk memprediksi tingkat kesepian siswa berdasarkan data profil media sosial. Algoritma ini dapat mempelajari data profil media sosial, seperti pola aktivitas, konteks konten yang dibagikan, interaksi sosial, ekspresi emosi, dan jaringan sosial. Selain itu, penggunaan data UCLA Loneliness Scale merupakan salah satu alat psikometri yang paling umum digunakan untuk mengukur tingkat kesepian dapat diintegrasikan dengan hasil prediksi model pembelajaran mesin untuk memberikan validitas tambahan dalam menilai kesepian. Penelitian ini menunjukkan hasil yang signifikan dalam peningkatan kinerja model klasifikasi setelah diterapkan metode Random Forest yang dioptimalkan dengan Optuna. Dalam penelitian ini, model digunakan untuk memprediksi tingkat kesepian berdasarkan profil media sosial, dan berhasil mencapai akurasi 90%. Hal ini menunjukkan bahwa model Random Forest yang dioptimalkan dengan Optuna memiliki potensi besar dalam mengklasifikasikan tingkat kesepian rendah dan tinggi pada siswa generasi Z.
Co-Authors Abdullah 'Alim Abdurrohim Musthofa Achmad Maulana Agus Harjoko Agus Santoso Ahmad Ihsanudin Ahmad Zainul Mafakhir Akbar, Kafi Kurnia Alfredo Pasaribu Alhafiz, Muhammad Ihza Ananda Surya, Archie Andi Hakim Arif Andika Pratama Anggi Ayu Ningtyas Anindya Putri Pradiptha Arif, Andi Hakim Asep Lukman Arip Hidayat Asmoro, Phaksi Bangun Bayu Raditya Nasution Chaerullah, Dhiesky Chalid, Iqbal Chandra, Joko Christian Dasril Aldo Dedy Mirwansyah Desena, Wahyu Desiawan, Masdar Dewantara, Erno Kurniawan Dwi Kristanto Dwi Kristanto Emil Salim Fadlan Amrullah Fahrullah Fahrullah Galih Gumilar Widhasmara Goenawan Brotosaputro Hanafi, Mohammad Afif Hari Soetanto Ikhsan, Rifqi Dainur Irennada Ismail Adi Susanto Jody Khaeri Diniari Khansa Khairunnisa Kurnianta, Kristana Lia Amellia Putri Lutfi Nukman Majid, Muhammad Farras Masdar Desiawan Maya Gian Sister Mochammad Andika Putra Mohammad Syafrullah Muhamad Refaldi Muhammad Agus Arianto Muhammad Agus Arianto Muhammad Ali Akbar Muhammad Arif Kurniawan Muhammad Fahrizal Muhammad Hamdi Sukriyandi Muhammad Verdiansyah Muharam, Asep Budiyana Nanda Arista Rizki Nariza Wanti Wulan Sari Nazori AZ Noor Ferdyansyah Nugroho, Ludi Nurwijayanti Obby Oktafianto Painem, Painem Pradana, Rizky Pradiptha, Anindya Putri Pramudita, Bagas Prayogi, Muhamad Nur Putri Diana Rahmat Kurniawan Rasyid, Annisa Ratna Kusumawardani Reka Dwi Syaputra Restu Maulunida Reva Ragam Santika Richki Hardi Riki Wijaya Rizki Darmawan, Dika Robby Suganda Rusdah Rusdah Saddam, M Amiruddin Setiyadi, Prambudi Suherman Achmad Syahrul, Ahmad Tan Wee Chang Tetlageni, Muhamad Ridho Triyono, Gandung Tulodo, Bernadeta Asri Rejeki Ummu Habibah Romlah Utomo Budiyanto Wati, Lisna Wirasno, Wirasno Yulia Nita Zainal A. Hasibuan Zulfikar Rosadi Zulqan, Helmi