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All Journal Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) JURNAL TEKNIK INFORMATIKA DAN SISTEM INFORMASI TRILOGI: Jurnal Ilmu Teknologi, Kesehatan, dan Humaniora JOINCS (Journal of Informatics, Network, and Computer Science) COREAI: Jurnal Kecerdasan Buatan, Komputasi dan Teknologi Informasi JAR'S (Journal of Advanced Research in Informatics) Transaction on Informatics and Data Science SINAR: Sinergi Pengabdian dan Inovasi untuk Masyarakat

Malik, Kamil

Unknown Affiliation

Author-ID : 816097

Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Environmental Science Mechanical Engineering Public Health Transportation Other

Published : 11 Documents Claim Missing Document

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Implementasi Algoritma Convolutional Neural Networks (CNN) untuk Klasifikasi Batik Rizal, Fathur; Hasyim, Fuadz; Malik, Kamil; Yudistira, Yudistira
COREAI: Jurnal Kecerdasan Buatan, Komputasi dan Teknologi Informasi Vol 2, No 2 (2021): Metaverse dan Masa Depan Interaksi Digital: Perspektif Teknologi dan Sosial
Publisher : Universitas Nurul Jadid

Batik adalah salah satu budaya khas Indonesia dan sudah diakui sebagai warisan budaya Internasional oleh UNESCO (The United Nations Educational, Scientific and Cultural Organization) pada tanggal 2 Oktober 2009. Batik telah menjadi warisan budaya turun temurun di seluruh Indonesia khususnya di daerah Jawa. Saat ini ada ratusan motif kain batik dari seluruh penjuru Indonesia. Banyaknya pola batik di Indonesia mengakibatkan sulitnya masyarakat mengidentifikasi motif pada batik. penelitian ini dapat mempermudah pengenalan pola batik. Salah satu teknologi kecerdasan buatan dengan sebutan artiﬁcial intelligence (AI) adalah pembelajaran mesin dengan menggunakan metode computer vision Salah satu model pembelajaran mesin tersebut adalah jaringan syaraf tiruan (JST) dengan menggunakan banyak lapisan, sehingga dengan adanya model tersebut maka dapat lebih baik lagi performa komputasi dengan menggunakan teknik Deep Learning. Metode yang digunakan adalah Convolutional Neural Networks (CNN) dengan melakukan klasiﬁkasi gambar pada batik berbasis komputer dengan memanfaatkan kecerdasan buatan (artiﬁcial intelligence). Hasil dari penelitian yang telah dilakukan pada pengujian terhadap 200 dataset dan 20 label diperoleh nilai akurasi yang tertinggi adalah “Batik Megamendung dan Batik Celup” dengan nilai akurasi 80% dan 60%, hasil accuracy yang diperoleh dari proses pelatihan model dari 200 epoch yang tertinggi adalah 90%.

Information Retrieval (IR) Pencarian Ide Pokok dalam Teks Artikel Olahraga Berbahasa Inggris Menggunakan Metode MMR (Maximum Marginal Relevance) Malik, Kamil; Jasri, Moh; Mashuri, Ahmad Sanusi
COREAI: Jurnal Kecerdasan Buatan, Komputasi dan Teknologi Informasi Vol 1, No 1 (2020): Keberlanjutan Teknologi Informasi: Green IT sebagai Solusi Ramah Lingkungan
Publisher : Universitas Nurul Jadid

Pengenalan wajah merupakan suatu teknologi dari komputer untuk mengidentifikasi wajah seseorang pada suatu gambar maupun video. Banyak metode yang bisa digunakan untuk pengenalan wajah antara lain metode fisherface, local binary pattern histogram, dan eigenface. Peneliti sebelumnya menerapkan pengenalan wajah menggunakan metode eigenface untuk mengidentifikasi wajah mahasiswa di Universitas Nurul Jadid. Akan tetapi, metode eigenface hanya fokus pada citra dengan objek tidak bergerak, sehingga belum bisa diterapkan pada video. Untuk itu, pada penelitian ini diusulkan suatu metode yang dapat mengidentifikasi wajah pada video yaitu metode haar cascade dan deep learning. Metode haar cascade merupakan suatu metode yang dapat mendeteksi posisi letak wajah pada suatu video dan metode deep learning untuk mengenali wajah yang sudah terdeteksi pada video. Hasil uji coba yang dilakukan metode haar cascade dapat mendeteksi adanya wajah pada video secara baik. Akan tetapi metode haar cascade juga mendeteksi yang bukan wajah pada data testing. Hasil dari uji coba pada gambar dengan metode haar cascade dan deep learning teridentifikasi secara benar dengan tingkat akurasi 99,6%. Hasil uji coba metode haar cascade dan deep learning pada video mahasiswa berhasil dilakukan jika komposisi warna dan tingkat cahayanya sama dengan data training dan jika tidak sesuai dengan data training maka tidak berhasil mengidentifikasi wajah mahasiswa pada video secara benar.

Evaluasi Model Jaringan Saraf Tiruan Berbasis LSTM dalam Memprediksi Fluktuasi Harga Bitcoin Sudriyanto, Sudriyanto; Faid, Mochammad; Malik, Kamil; Supriadi, Ahmad
Journal of Advanced Research in Informatics Vol 2 No 2 (2024): Journal of Advanced Research in Informatics
Publisher : Fakultas Teknik, Universitas Wiraraja

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24929/jars.v2i2.3398

Amid the highly volatile fluctuations in the cryptocurrency market, the ability to accurately predict Bitcoin prices becomes crucial for investors and financial analysts. This study aims to develop a predictive model using Long Short-Term Memory (LSTM) Neural Networks, a specific form of recurrent neural network, to predict Bitcoin prices. Historical data on daily closing prices of Bitcoin from 2015 to 2023 was used to train and test the model. Following data preprocessing, which included normalization and the creation of a time series dataset, the LSTM model was constructed with two LSTM layers and two dense layers to enhance the predictive analysis. The model was trained with the data split into 80% for training and 20% for testing. Results show that the LSTM model was able to produce fairly accurate predictions with a low loss value on the test data. Further evaluation through comparison with baseline models showed significant improvements in predictive accuracy. This research demonstrates the potential application of advanced machine learning techniques in financial analysis, particularly in predicting the prices of highly volatile assets like Bitcoin. With continuous improvements to the model architecture and parameter optimization, Bitcoin price predictions could become more reliable, helping stakeholders make more informed investment decisions.

Implementasi Algoritma K-Means Clustering Untuk Pengelompokan Loyalitas Pelanggan Berbasis Web di UD.Majutoto Malang Widad, Zeda Al; Sudriyanto, Sudriyanto; Malik, Kamil
JATISI Vol 11 No 4 (2024): JATISI (Jurnal Teknik Informatika dan Sistem Informasi)
Publisher : Universitas Multi Data Palembang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35957/jatisi.v11i4.8898

Performance evaluation of a company often relies on its profitability, significantly impacted by the presence of active customers. Customer segmentation into loyal and non-loyal categories using the K-Means Clustering algorithm assists in developing subsequent strategies, including tailored incentives based on customer loyalty levels. Applying this algorithm at UD. Majutoto Malang, customers were segmented based on box purchases into three clusters with random initial centers. Out of 624 data points, the segmentation resulted in 62 non-loyal customers, 463 highly loyal customers, and 99 moderately loyal customers. The clustering accuracy was evaluated using the Davies-Bouldin Index (DBI), which yielded a score of 0.01. A DBI value close to 0 indicates good clustering quality.

Sistem Informasi Monitoring Manajemen Penggunaan Aset Pondok Pesantren Nurul Anwar Mengunakan Framework Codeigniter Malik, Kamil; Hudaya, Kharisman Kholid; Purnomo, Eko; Sya'roni, Wahab
COREAI: Jurnal Kecerdasan Buatan, Komputasi dan Teknologi Informasi Vol 4, No 1 (2023): Metaverse dan Masa Depan Interaksi Digital: Perspektif Teknologi dan Sosial
Publisher : Universitas Nurul Jadid

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33650/coreai.v4i1.4463

Visi Pondok Pesantren Nurul Anwar adalah mencetak manusia yang Beriman, Bertaqwa, Berakhlak Al-Karimah, Berilmu, Berwawasan Luas, Berpandangan Ke Depan, Cakap, Terampil, Mandiri, Kreatif, Memiliki Etos Kerja, Toleran, bertanggung jawab kemasyarakatan, serta berguna bagi Agama, Bangsa, dan Negara. Misi pondok pesantren termasuk Penanaman keimanan, ketaqwaan kepada Allah, pembinaan Akhlak Al-Karimah, pendidikan keilmuan dan pengembangan wawasan, pengembangan bakat dan minat, pembinaan keterampilan dan keahlian, pengembangan kewirausahaan dan kemandirian, serta penanaman kesadaran hidup sehat, kepedulian terhadap lingkungan, dan tanggung jawab kemasyarakatan dan kebangsaan. Pondok pesantren ini menghadapi beberapa permasalahan terkait aset transportasi, seperti pengembalian kendaraan, keterlambatan servis kendaraan, dan pencatatan data kendaraan serta profil karyawan secara manual menggunakan media kertas. Oleh karena itu, penelitian ini mengusulkan pembuatan Sistem Informasi yang memanfaatkan Framework Codeigniter untuk memberikan kemudahan dalam pendataan dan pengelolaan aset-aset pesantren, serta memungkinkan pengecekan berkala terhadap aset bergerak yang digunakan. Manfaatnya adalah memberikan kemudahan, peningkatan efisiensi, dan kenyamanan bagi pengguna aset bergerak operasional Pesantren Nurul Anwar. Diharapkan dengan adanya aplikasi ini, proses pengelolaan dan pendataan aset pondok pesantren dapat ditingkatkan ke depannya.

AI CHATBOT IMPLEMENTATION FOR NURUL JADID UNIVERSITY WEBSITE USING LSTM ALGORITHM Sudriyanto, Sudriyanto; Malik, Kamil; Jamal, Jamal
Journal of Advanced Research in Informatics Vol 3 No 2 (2025): Journal of Advanced Research in Informatics
Publisher : Fakultas Teknik, Universitas Wiraraja

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24929/jars.v3i2.4163

The rapid advancement of technology has brought significant changes in various aspects of life, including the education sector. As an educational institution, Nurul Jadid University must adopt the latest technology to enhance efficiency and service, particularly in responding to the increasing volume of inquiries and information needs from the public and parents before enrolling their children. A chatbot, as part of Natural Language Processing (NLP) based on Artificial Intelligence (AI), is designed to interact with users through text or voice, providing fast, accurate, and continuous service. The Long Short-Term Memory (LSTM) algorithm in deep learning is utilized for text data prediction and classification. In this research, the data consists of tags, patterns, and responses obtained manually from the official Nurul Jadid University website and then preprocessed to develop the chatbot model. The core component of this model is the embedding layer, which assigns vector values to each word in the processed text data. The model training results indicate an accuracy of 99.32% and a loss of 12.57%, demonstrating that the model performs well without overfitting or underfitting, making it suitable for testing and deployment. Thus, the LSTM-based chatbot serves as an effective virtual assistant to help the public, prospective students, and current students access information more easily and efficiently.

Diagnosa Pharyngitis Menggunakan Metode K-Nearest Neighbor (K-NN) di Puskesmas Leces Probolinggo Malik, Kamil; Pratama, Yoga; Nisa', Khoirun
TRILOGI: Jurnal Ilmu Teknologi, Kesehatan, dan Humaniora Vol 2, No 3 (2021): Pengembangan Teknologi dan Kesehatan di Lembaga Keagamaan
Publisher : Universitas Nurul Jadid

Puskesmas Leces menyimpan jutaan data rekam medis pasien yang selama ini berkunjung, baik pasien rawat jalan maupun rawat inap. Data mining sebagai ilmu baru yang memiliki kegiatan untuk mengektraksi data di suatu kumpulan data yang besar/banyak, sangat potensial untuk diterapkan. Salah satu keluhan yang memiliki frekuensi terbanyak dalam kunjungannya adalah pasien dengan diagnosa pharyngitis. penderita pharyngitis memiliki kemiripan dengan gejala tonsilitis, laringitis, atau keradangan pada tenggorokan. Dalam mendiagnosa, selain dibutuhkannya kecermatan petugas kesehatan, records pada rekam medis yang selama ini mencatat perilaku pasien dengan diagnosa yang sama tentu dapat memudahkan petugas kesehatan untuk memberikan keputusan klinis. Dengan mengimplementasikan metode K-Nearest Neighbor yang bertujuan untuk memberikan rekomendasi diagnosa berdasarkan atribut anamnesa dan hasil cek suhu tubuh pasien. Pengujian dilakukan dengan membandingkan hasil diagnosa yang sudah diverifikasi oleh akademik kampus dengan hasil dari penghitungan k-NN. Dari pengujian dataset yang terdiri dari 95 data training dan 15 data uji dengan label diagnosa pharyngitis dan tonsilitis, dihasilkan nilai akurasi optimal dengan k=3 yaitu 86,67%. Maka dapat disimpulkan bahwa metode k-NN mampu melakukan proses diagnosa pharyngitis melalui database rekam medis pasien di puskesmas leces

Detection of Eight Skin Diseases Using Convolutional Neural Network with MobileNetV2 Architecture for Identification and Treatment Recommendation on Android Application Furqon, Ainul; Malik, Kamil; Fajri, Fathorazi Nur
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol. 10 No. 2 (2024): June
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v10i2.28817

Skin diseases are common in Indonesia due to the tropical climate, high population density, and low public awareness about skin health. These diseases are often caused by infections, chemical contamination, or other external factors and typically develop internally before becoming visible, with contact dermatitis being the most frequently reported condition. To address this issue, this research proposes the use of Artificial Intelligence (AI), specifically Convolutional Neural Network (CNN) with the MobileNetV2 architecture, to detect eight types of skin diseases, namely cellulitis, impetigo, athlete's foot, nail fungus, ringworm, cutaneous larva migrans, chickenpox, and shingles. MobileNetV2 was chosen for its efficiency and high accuracy in mobile applications. The methodology involves developing a detection system using CNN MobileNetV2, integrated into an Android application to identify skin diseases and provide treatment recommendations. The dataset was collected, labeled, resized, and normalized to meet the model requirements. After training, the model was tested using a separate dataset to ensure its generalization ability and was finally integrated into the Android application. This application allows users to detect skin diseases and receive treatment advice directly. The research results show that the CNN MobileNetV2 model achieves high accuracy in classifying the eight types of skin diseases, with stable performance over several training epochs. Evaluation of the test dataset revealed an overall accuracy of 97%, with high precision, recall, and F1-score for all disease classes. The application achieved an accuracy of 84% on general data, demonstrating its practical utility. However, the need for real-time updates of treatment information was identified as a limitation. This research advances skin disease detection technology and improves public access to accurate healthcare services. Future studies should focus on real-time treatment information updates and expanding the range of detectable diseases to enhance skin disease application.

Validation of New Student Registration Documents at Nurul Jadid University Using Convolutional Neural Network Fajri, Fathorazi Nur; Pratamasunu, Gulpi Qorik Oktagalu; Malik, Kamil
Transactions on Informatics and Data Science Vol. 1 No. 2 (2024)
Publisher : Department of Informatics, Faculty of Da'wah, UIN Saizu Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24090/tids.v1i2.12281

Every year, Nurul Jadid University admits new students by registering them using the website. Each prospective new student can fill in data independently and upload documents such as Deeds, Family Register, Identity Cards, Diplomas, and SKHU. Often, prospective new students need clarification in uploading documents; for example, the place for uploading ID cards is filled with uploading diplomas and vice versa. It causes the uploaded data not to match the place or group. Today, no document validation technique can match these types of documents. Therefore, a way is needed to overcome this problem. One way to recognize the document type is by its visual form or image. There are several methods for identifying an image, namely deep learning and neural network models. Where the convolutional neural network is known to be fast in processing data in images, this research aims to validate documents on new student registration data with a deep learning method, namely convolutional neural network (CNN). The experimental results show that the proposed method can classify the Nurul Jadid University new student registration documents with an accuracy rate of 0.91, such as the birth certificate at 0.97, diploma documents at 0.88, Family card documents at 0.88, identity cards at 0.84, exam result certificate with an accuracy 0.94.

Decision Support System for Determining the Best Santri Using the SAW Method: Sistem Pendukung Keputusan Penentuan Santri Terbaik Dengan Metode SAW Malik, Kamil; Sya’roni, Wahab
JOINCS (Journal of Informatics, Network, and Computer Science) Vol. 5 No. 2 (2022): November
Publisher : Universitas Muhammadiyah Sidoarjo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21070/joincs.v5i2.1599

The I'dadiyah Az-zainiyah institution is an institution devoted to new students of the Nurul Jadid Islamic Boarding School Paiton Probolinggo at the junior and senior high school levels. This institution is devoted to new students who do not have a foundation in religion. To give appreciation to the students of the I'dadiyah Az-zainiyah Institute so that Santi is more enthusiastic to study religious knowledge every semester, the best students will be selected to be awarded in the form of scholarships. However, from the large number of participants, the management of the I'dadiyah Az-zainiyah Institution has difficulty determining the best participants because there are several variables that must be compared between one santri and another, so there is often a delay in the announcement of the best participants which results in delays in prospective students who get scholarships. from several schools under the auspices of the Nurul Jadid Islamic Boarding School. Seeing this, it is necessary to have a method that can make it easier for the administrators of the I'dadiyah Az-zainiyah Islamic Boarding School Nurul Jadid to determine the best new students or new students. The method taken by the researcher is the Simple Additive Weighting (SAW) decision support system method.

Co-Authors Badir, Muhammad Eko Purnomo Fathorazi Nur Fajri Fathur Rizal Furqon, Ainul Gulpi Qorik Oktagalu Pratamasunu Hasyim, Fuadz Hudaya, Kharisman Kholid Jamal Jamal Jasri, Moh. Mashuri, Ahmad Sanusi Miftahul Arifin Mochammad Faid Nisa', Khoirun Supriadi, Ahmad Sya'roni, Wahab Sya’roni, Wahab Widad, Zeda Al Yoga Pratama Yudistira Yudistira Zainal Arifin

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