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All Journal Jurnal Ilmu Komputer dan Informasi Jurnal Buana Informatika JUTI: Jurnal Ilmiah Teknologi Informasi 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 MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer 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
Aerospace Engineering Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry 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.
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.
REDUKSI DIMENSI FITUR MENGGUNAKAN ALGORITMA ALOFT UNTUK PENGELOMPOKAN DOKUMEN Mamluatul Hani’ah; Chastine Fatichah; Diana Purwitasari
JUTI: Jurnal Ilmiah Teknologi Informasi Vol 14, No. 2, Juli 2016
Publisher : Department of Informatics, Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j24068535.v14i2.a573

Abstract

Pengelompokan dokumen masih memiliki tantangan dimana semakin besar dokumen maka akan menghasilkan fitur yang semakin banyak. Sehingga berdampak pada tingginya dimensi dan dapat menyebabkan performa yang buruk terhadap algoritma clustering. Cara untuk mengatasi masalah ini adalah dengan reduksi dimensi. Metode reduksi dimensi seperti seleksi fitur dengan metode filter telah digunakan untuk pengelompokan dokumen. Akan tetapi metode filter sangat tergantung pada masukan pengguna untuk memilih sejumlah n fitur teratas dari keseluruhan dokumen. Algoritma ALOFT (At Least One FeaTure) dapat menghasilkan sejumlah set fitur secara otomatis tanpa adanya parameter masukan dari pengguna. Karena sebelumnya algoritma ALOFT digunakan pada klasifikasi dokumen, metode filter yang digunakan pada algoritma ALOFT membutuhkan adanya label pada kelas sehingga metode filter tersebut tidak dapat digunakan untuk pengelompokan dokumen. Pada penelitian ini diusulkan metode reduksi dimensi fitur dengan menggunakan variasi metode filter pada algoritma ALOFT untuk pengelompokan dokumen. Sebelum dilakukan proses reduksi dimensi langkah pertama yang harus dilakukan adalah tahap preprocessing kemudian dilakukan perhitungan bobot tfidf. Proses reduksi dimensi dilakukan dengan menggunakan metode filter seperti Document Frequency (DF), Term Contribution (TC), Term Variance Quality (TVQ), Term Variance (TV), Mean Absolute Difference (MAD), Mean Median (MM), dan Arithmetic Mean Geometric Mean (AMGM). Selanjutnya himpunan fitur akhir dipilih dengan algoritma ALOFT. Tahap terakhir adalah pengelompokan dokumen menggunakan dua metode clustering yang berbeda yaitu k-means dan Hierarchical Agglomerative Clustering (HAC). Dari hasil ujicoba didapatkan bahwa kualitas cluster yang dihasilkan oleh metode usulan dengan menggunakan algoritma k-means mampu memperbaiki hasil dari metode VR.
Proliferative Diabetic Retinopathy Detection Using Convolutional Neural Network with Enhanced Retinal Image Wilda Imama Sabilla; Mamluatul Hani'ah; Ariadi Retno Tri Hayati Ririd; Astrifidha Rahma Amalia
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol. 25 No. 1 (2025)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v25i1.4976

Abstract

Proliferative Diabetic Retinopathy (PDR) is the most severe stage of Diabetic Retinopathy (DR), carrying the highest risk of complications. Automatic detection can help provide earlier and more accurate PDR diagnosis, but prediction accuracy may decline due to limitations in retinal images. Therefore, image enhancement techniques are often applied to improve DR classification. This study aims to detect PDR from retinal images using Convolutional Neural Networks (CNNs) and to evaluate the impact of three enhancement methods. This research method is based on a CNN architecture, including ResNet34, InceptionV2, and DenseNet121, as well as enhancement methods such as CLAHE, Homomorphic Filtering (HF), and Multiscale Contrast Enhancement (MCE). The results of this research show that CNN performance varies across architectures and enhancement methods. The highest performance was achieved using ResNet34 with HF, yielding an accuracy of 0.976, precision of 0.934, and recall of 0.904. CLAHE generally improved performance across architectures, achieving the best average accuracy of 0.953, whereas MCE decreased classification accuracy. Overall, the findings highlight the importance of selecting appropriate enhancement methods to improve PDR detection accuracy. Implementing such systems in clinical screening could help reduce the risk of vision impairment among diabetic patients.
Google Trends and Technical Indicator based Machine Learning for Stock Market Prediction Mamluatul Hani'ah; Moch Zawaruddin Abdullah; Wilda Imama Sabilla; Syafaat Akbar; Dikky Rahmad Shafara
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol. 22 No. 2 (2023)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v22i2.2287

Abstract

The stock market often attracts investors to invest, but it is not uncommon for investors to experience losses when buying and selling shares. This causes investors to hesitate to determine when to sell or buy shares in the stock market. The accurate stock price prediction will help investors to decide when to buy or sell their shares. In this study, we propose a new approach to predicting stocks using machine learning with a combination of features from stock price features, technical indicators, and Google trends data. Three well-known machine learning algorithms such as Support Vector Regression (SVR), Multilayer Perceptron (MLP), and Multiple Linear regression are used to predict future stock prices. The test results show that the SVR outperformed the MLP and Multiple Linear Regression to predict stock prices for Indonesian stocks with an average MAPE is 0.50%. The SVR can predict the stock price close to the actual price.
Development of a Meeting Room Reservation System in the Food and Beverage Industry Achmad Aly Abdulloh; Mamluatul Hani’ah; Vivi Nur Wijayaningrum
Journal of INISTA Vol 8 No 2 (2026): May 2026
Publisher : LPPM Institut Teknologi Telkom Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20895/inista.v8i2.2050

Abstract

The manual process of meeting room reservations often takes considerable time and creates inefficiency, especially when coordinating schedules among multiple users. In addition, searching for meeting-related information is not yet integrated, and the absence of automatic reminders often causes users to miss scheduled meetings. To address these issues, a web-based system was developed to manage meeting room reservations digitally. The system includes an information search feature using a chatbot and automatic reminders integrated with Google Calendar or Outlook. The prototype method was applied in the development process. To ensure the system meets user needs, evaluations were carried out through functional testing, the User Experience Questionnaire (UEQ), and time efficiency testing. The functional testing results show that the system works properly and meets user requirements. Meanwhile, UEQ results indicate that all aspects Attractiveness, Perspicuity, Efficiency, Dependability, and Stimulation achieved the “Excellent” category. The highest score was for Stimulation at 2.531, suggesting the system provides an enjoyable, non-monotonous experience that fosters user engagement. In terms of efficiency, manual reservations required between 1,245 and 1,560 seconds, depending on the responsiveness of the admin or receptionist. After system implementation, booking time was reduced to 35 – 50 seconds. This shows an improvement in reservation efficiency of about 96%.
Pengembangan Deteksi Pesan Spam pada Website Inti Everspring Indonesia Menggunakan Algoritma Support Vector Machine Syafaat Akbar; Mamluatul Hani'ah; Imam Fahrur Rozi
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.
Hyperparameter Tuning Metode Long Short-Term Memory (LSTM) untuk Prediksi Harga Bawang Merah di Pasar Tradisional Jawa Timur M. Latifur Rahman; Mamluatul Hani'ah; Muhammad Afif Hendrawan
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.v8i2.976

Abstract

Shallots are one of the essential food commodities in East Java, the price of which frequently fluctuates due to seasonal influences and distribution factors. This price uncertainty often weakens the community's purchasing power and makes it difficult for farmers and sellers to make decisions. Therefore, this study proposes a daily shallot price prediction model for nine regions in East Java using the Long Short-Term Memory (LSTM) method with hyperparameter tuning to obtain the best-performing model. This study utilizes daily price data from PIHPS (Pusat Informasi Harga Pangan Strategis / Strategic Food Price Information Center) over four years, from January 1, 2021, to December 31, 2024. The research stages begin with data preprocessing, which includes imputing missing values to ensure continuity of the time sequence, data normalization, and arrangement of data based on temporal order (time series). To obtain the optimal model, the data was split into 80% for training and 20% for testing. Based on the experimental results, the optimal model configuration was found using a Window Size of 7 (the last seven days of data), 114 Epochs, and a Batch Size of 64. Furthermore, the reliability of the model was measured using MAPE, where the average across all regions fell below 10%, which is classified as "Very Good" based on MAPE interpretation standards. Additional testing using 2025 data as an independent dataset further confirmed that the model remains consistent and stable, as evidenced by an average MAPE of 3.10% across the nine regions.
Co-Authors Achmad Aly Abdulloh Adhisuwignjo, Supriatna Aflah, Darin Zahira Agus Zainal Arifin Agus Zainal Arifin Ahmadi Yuli Ananta Alif Akbar Fitrawan Alif Akbar Fitrawan, Alif Akbar Annisa Puspa Kirana Annisa Puspa Kirana Ariadi Retno Tri Hayati Ririd Arie Rachmad Syulistyo Arwin Datumaya Wahyudi Sumari Aryo Harto Aryo Harto Astrifidha Rahma Amalia Aziza, Nadia Layra Budi Harijanto, Budi Budiprasetyo, Gunawan Cahya Rahmad Candra Bella Vista Chastine Fatichah 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 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 Imam Fahrur Rozi Irfan Thalib Alfarid Irsyad Arif Mashudi Komalasari, Nita Kusumaning, Ika Luqman Affandi Luqman Affandi M. Hasyim Ratsanjani M. Latifur Rahman Maulidia, Irma Moch Zawaruddin Abdullah Mochammad Hairullah Muhammad Afif Hendrawan Nita Komalasari Ni’ma Shoumi, Milyun Noprianto Noprianto Nurfaidah Nurfaidah Pratama, Muhammad Irgy Rahman, Muhammad Arif Rakhmat Arianto Rokhimatul Wakhidah Septianda Reza Maulana Sofyan Noor Arief Syafaat Akbar Syafaat Akbar Triana Fatmawati Umi Laili Yuhana Vipkas Al Hadid Firdaus Vivi Nur Wijayaningrum Wilda Imama Sabilla Yan Watequlis Syaifudin Yogi Kurniaawan Yogi Kurniaawan, Yogi Yogi Kurniawan Yogi Kurniawan Yoppy Yunhasnawa