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All Journal Jurnal Ilmu Komputer dan Informasi Jurnal Buana Informatika Jurnal Ilmiah Kursor Jurnas Nasional Teknologi dan Sistem Informasi JOIV : International Journal on Informatics Visualization JURNAL MEDIA INFORMATIKA BUDIDARMA Conference SENATIK STT Adisutjipto Yogyakarta Applied Information System and Management Jurnal Teknologi Informasi dan Multimedia G-Tech : Jurnal Teknologi Terapan Journal of Informatics, Information System, Software Engineering and Applications (INISTA) Jurnal Pengabdian Polinema Kepada Masyarakat Prosiding Seminar Nasional Pengabdian Kepada Masyarakat Jurnal Informatika Polinema (JIP) Journal of Multidisciplinary Inquiry in Science, Technology and Educational Research Jurnal Pengabdian Kepada Masyarakat Jurnal Komtika (Komputasi dan Informatika)
Mamluatul Hani’ah
Politeknik Negeri Malang Malang
Aerospace Engineering Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Mechanical Engineering Public Health Social Sciences Other

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Preserving Indigenous Indonesian Batik Motif Using Machine Learning and Information Fusion Sumari, Arwin Datumaya Wahyudi; Aziza, Nadia Layra; Hani'ah, Mamluatul
JOIV : International Journal on Informatics Visualization Vol 9, No 5 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.5.3714

Abstract

Preserving Indonesia’s indigenous cultural heritage in the form of Batik with various motifs to maintain the nation’s continuity from generation to generation. Hundreds of Batik motifs are spread across multiple regions of Indonesia, along with their unique names and meanings, where each motif has a cultural and historical meaning behind it. The distinctive patterns of Batik motifs challenge the community to remember and distinguish them, so it is crucial to have an intelligent system. This study designed and implemented a Batik motif classification system based on machine learning’s Support Vector Machine (SVM) with a Radial Basis Function (RBF) kernel. The primary key to classifier performance is features. An assessment was carried out on the performance of two feature models: single features and fused features. The Gray Level Co-occurrence Matrix (GLCM) produces the texture features of the Batik motif, and the Moment Invariant (MI) is used to create the shape features of Batik motifs. The Union Fusion and XOR operators produce a single fused feature of the two features. The proposed combination of techniques, namely SVM and GLCM, outperforms the combination scenario of Multi Texton Histogram (MTH), Multi Texton Co-Occurrence Descriptor (MTCD), Multi Texton Co-occurrence Histogram (MTCH) with SVM, and the combination of GLCM with 1-NN as well as the combination techniques that employed information fusion. The experiment results showed that the proposed combination technique achieved an accuracy of 97%. It can be concluded that SVM (RBF) with GLCM yields the best Batik motif recognition system.
Prediksi Harga Saham Syariah Menggunakan Algoritma Long Short-Term Memory (LSTM) Budiprasetyo, Gunawan; Hani'ah, Mamluatul; Aflah, Darin Zahira
Jurnal Nasional Teknologi dan Sistem Informasi Vol 8 No 3 (2022): Desember 2022
Publisher : Departemen Sistem Informasi, Fakultas Teknologi Informasi, Universitas Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25077/TEKNOSI.v8i3.2022.164-172

Abstract

Semakin pesatnya perkembangan pasar saham di Indonesia membuat semakin banyak investor yang bergabung di bursa saham. Indonesia pada tahun 2011 meluncurkan saham syariah dimana harga saham syariah dapat mengalami kenaikan dan penurunan. Hal ini tentunya harus diwaspadai oleh investor, agar investor tidak mengalami kerugian dalam jual-beli saham.  Untuk itu, prediksi harga sahan menjadi salah satu upaya untuk menentukan nilai dari suatu saham di masa kedepannya. Pada penelitian ini, prediksi saham dilakukan dengan menggunakan metode Long Short-Term Memory dalam memprediksi harga saham. Dilakukan uji coba dengan menggunakan beberapa parameter pada layers, epoch dan time step untuk mendapatkan model prediksi yang optimal. Arsitektur dari LSTM yang digunakan pada penelitian ini menggunakan multiple layer LSTM dengan empat dan delapan layer yang masing-masing layer memiliki 96 neurons. Terdapat satu Dense layer yang berfungsi mengubah output dari layer sebelumnya menjadi nilai hasil prediksi. Hasil eksperimen menunjukkan bahwa Long Short-Term Memory dapat digunakan untuk melakukan prediksi harga saham dengan akurat, jumlah layer mempengaruhi MAPE yang dihasilkan. LSTM dengan jumlah layer 8 memiliki performa yang lebih baik. Pada PT Aneka Tambang Tbk didapatkan model terbaik dengan nilai MAPE sebesar 2,64. Untuk emiten Erajaya Swasembada Tbk didapatkan nilai MAPE sebesar 2,24. Untuk Kalbe Farma didapatkan nilai MAPE sebesar 1,51. Untuk Semen Indonesia didapatkan nilai MAPE sebesar 1,83. Sedangkan pada Wijaya Karya didapatkan nilai MAPE sebesar 2,66.
Design and Implementation KP-SPAMS Transaction Information System utilizing Laravel Framework and Extreme Programming Methodology Abdullah, Moch Zawaruddin; Hani'ah, Mamluatul; Yunhasnawa, Yoppy; Wakhidah, Rokhimatul
Journal of INISTA Vol 7 No 1 (2024): November 2024
Publisher : LPPM Institut Teknologi Telkom Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20895/inista.v7i1.1645

Abstract

The Community-Based Drinking Water and Sanitation Management Group (KP-SPAMS) oversees the Community-Based Drinking Water and Sanitation Provision Program (PAMSIMAS), which is essential for providing clean water services to rural areas. Nevertheless, KP-SPAMS continues to face challenges related to operational transaction management, such as the documentation of customer data, water usage, invoicing, and financial reporting. This research aims to develop a web-based transaction information system, utilizing the Laravel framework and the Extreme Programming methodology, to meet the specific requirements of KP-SPAMS Sumber Waras located in Ngenep Village, Malang Regency. The Extreme Programming methodology facilitates adaptable and cooperative software development, enabling quick responses to evolving customer requirements. The system's primary functionalities are customer registration, water usage recording, automatic billing, and payment reporting. The implementation results indicate that this system may enhance operational efficiency, accountability, and traceability of all transaction processes in KP-SPAMS, facilitating improved decision-making and superior service quality for the community. User Acceptance Testing results show that 80% of users rated the system positively, with 53.33% agreeing and 26.67% strongly agreeing that the system meets their needs and provides a satisfactory experience. Only 6.67% of responses indicated dissatisfaction, and no respondents strongly disagreed, demonstrating that the system aligns well with user expectations and offers a solid foundation for future improvements.
Mask Detection App Uses Haar Cascade and Convolutional Neural Network to Alert Comply with Health Protocols Rahmad, Cahya; Nurfaidah, Nurfaidah; Adhisuwignjo, Supriatna; Hani’ah, Mamluatul
Applied Information System and Management (AISM) Vol. 6 No. 2 (2023): Applied Information System and Management (AISM)
Publisher : Depart. of Information Systems, FST, UIN Syarif Hidayatullah Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15408/aism.v6i2.31396

Abstract

This study aims to identify the face of a person whether wearing a mask or not wearing a mask accompanied by an appeal to the importance of wearing a mask. The contribution of this paper to science is to provide an overview of the results of accuracy, precision, recall used by the method used with data that can be accessed by many people, so that it can be developed further or can be compared. This system uses two techniques, namely the classification of whether a person is wearing a mask or not using the Convolutional Neural Network (CNN) model. The architecture used is DenseNet-12 to detect human face objects. The data used has a total of 2332 data sets, 200 of which were retrieved manually as research objects, and the rest were obtained from Kaggle. All data is evaluated using the camera in real-time. The test results show that testing scenario one has the highest score with an accuracy of 85% while testing scenario two gets results of 80%, the precision value in testing scenario one gets results of 75%, and testing scenario two has results of 88%. Scenarios 1 and 2 also have the same recall value of 100%. Based on the data analysis, it can be concluded that the use of the Haar Cascade approach and the Convolutional Neural Network with the DenseNet-121 architecture produces good performance in the case of real-time detection of masked and non-masked facial objects.
Sistem Pakar Diagnosa Hama Penyakit Tanaman Kentang Dengan Metode Forward Chaining Rahman, Muhammad Arif; Rozi, Imam Fahrur; Hani'ah, Mamluatul
Jurnal Komtika (Komputasi dan Informatika) Vol 8 No 1 (2024)
Publisher : Universitas Muhammadiyah Magelang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31603/komtika.v8i1.11128

Abstract

Potatoes (Solanum tuberosum L.) are a priority vegetable crop due to their high domestic trade value and export potential. Potatoes are used for various purposes, both as a vegetable and as a carbohydrate substitute. In addition to being used as a vegetable, potatoes are also utilized as raw materials in the food industry, such as chips, potato flour, and potato starch. Due to the relatively low temperature requirement (20-22°C) for tuber formation, potato cultivation areas in Indonesia are generally located in mountainous regions. One of the potato commodity centers is in the city of Batu, particularly in the Bumiaji District. According to vegetable crop potential data from the Batu City extension program in 2022, the area planted with potatoes is 485.2 hectares with a production potential of 968 tons. Since potato plants are more susceptible to pests and diseases, substandard maintenance can lead to low harvest yields, poor sales, and even crop failure. This issue has led to the development of an application for diagnosing potato pests. The expert system uses forward chaining methods and is web-based. The expert system processes facts answered by users of the potato application, diagnoses the symptoms present, and generates diagnostic results in the form of solutions for the diagnosed potato plant diseases or pests. With the availability of an expert system application for diagnosing potato plant diseases and pests, the limitation of expert manpower is no longer a hindrance for potato farmers. Recommendations and information regarding potato diseases and pests can be obtained online without the need to consult a specialist.
Pengembangan Deteksi Pesan Spam pada Website Inti Everspring Indonesia Menggunakan Algoritma Support Vector Machine Akbar, Syafaat; Hani'ah, Mamluatul; Rozi, Imam Fahrur
Jurnal Teknologi Informasi dan Multimedia Vol. 8 No. 2 (2026): May
Publisher : Sekawan Institut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35746/jtim.v8i2.872

Abstract

The development of information technology has driven the growth of email-based communica-tion in business environments, including at Inti Everspring Indonesia. However, the high volume of incoming emails increases the potential for spam messages that may disrupt work effectiveness and data security. This study develops a spam detection system on the company’s website by ap-plying the Support Vector Machine (SVM) algorithm. SVM was selected because of its ability to perform text classification efficiently. The dataset used in this research comes from the company’s internal emails, consisting of labeled spam and non-spam messages. Since the dataset is imbal-anced, an oversampling process was applied, followed by text preprocessing steps including case folding, tokenization, removal of stop words, symbols, numbers, and stemming. The model was then trained using the SVM algorithm, and its performance was evaluated using several metrics: accuracy, recall, precision, and F1-score. Based on the experiments, the SVM-based spam detec-tion model achieved 100% precision, 100% recall, and a 100% F1-score. To validate the reliabil-ity of the algorithm, SVM performance was compared with BERT and Naïve Bayes. BERT achieved 96% accuracy, and Naïve Bayes achieved 97% accuracy. These results indicate that SVM is capable of classifying messages accurately, and SVM outperforms both algorithms.
Co-Authors Adhisuwignjo, Supriatna Aflah, Darin Zahira Agus Zainal Arifin Agus Zainal Arifin Akbar, Syafaat Alif Akbar Fitrawan Alif Akbar Fitrawan, Alif Akbar Ananta, Ahmadi Yuli Annisa Puspa Kirana Annisa Puspa Kirana Arie Rachmad Syulistyo Arwin Datumaya Wahyudi Sumari Aryo Harto Aryo Harto Aziza, Nadia Layra Budi Harijanto, Budi Budiprasetyo, Gunawan Cahya Rahmad Candra Bella Vista Christian Sri Kusuma Aditya Christian Sri Kusuma Aditya Christian Sri kusuma Aditya, Christian Sri kusuma Darin Zahira Aflah Deasy Sandhya E.I. Diana Purwitasari Diana Purwitasari Dika Rizky Yunianto Dikky Rahmad Shafara Dwi Puspitasari Gunawan Budiprasetyo Hayati, Ariadi Retno Iftitah Hidayati Ika Kusumaning Putri Ika Kusumaning Putri Ilham Sinatrio Gumelar Imam Fahrur Rozi Irfan Thalib Alfarid Irsyad Arif Mashudi Komalasari, Nita Kusumaning, Ika Luqman Affandi Luqman Affandi M. Hasyim Ratsanjani Maulidia, Irma Moch Zawaruddin Abdullah Mochammad Hairullah Nita Komalasari Noprianto Noprianto Nurfaidah Nurfaidah Nurfaidah Nurfaidah Pratama, Muhammad Irgy Rahmad, Cahya Rahman, Muhammad Arif Rakhmat Arianto Rokhimatul Wakhidah Septianda Reza Maulana Shoumi, Milyun Ni’ma Sofyan Noor Arief Supriatna Adhisuwignjo Syafaat Akbar Triana Fatmawati Vipkas Al Hadid Firdaus Vivi Nur Wijayaningrum Wilda Imama Sabilla Yan Watequlis Syaifudin Yogi Kurniaawan Yogi Kurniaawan, Yogi Yogi Kurniawan Yogi Kurniawan Yoppy Yunhasnawa Yuhana, Umi Laili