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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 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 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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REDUKSI DIMENSI FITUR MENGGUNAKAN ALGORITMA ALOFT UNTUK PENGELOMPOKAN DOKUMEN Hani’ah, Mamluatul; Fatichah, Chastine; Purwitasari, Diana
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.
Comparison of Feature Extraction in Support Vector Machine (SVM) Based Sentiment Analysis System Rozi, Imam Fahrur; Maulidia, Irma; Hani’ah, Mamluatul; Arianto, Rakhmat; Yunianto, Dika Rizky; Ananta, Ahmadi Yuli
Jurnal Ilmiah Kursor Vol. 13 No. 1 (2025)
Publisher : Universitas Trunojoyo Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21107/kursor.v13i1.417

Abstract

Sentiment analysis plays a crucial role in natural language processing by identifying and categorizing opinions or emotions conveyed in textual data. It is widely applied across diverse fields such as product review analysis, social media monitoring, and market research. To enhance the accuracy and reliability of sentiment classification, various methods and feature extraction techniques have been explored. This study investigates the use of Support Vector Machine (SVM) for sentiment analysis, comparing three feature extraction techniques: Term Frequency-Inverse Document Frequency (TF-IDF), Bag of Words (BoW), and Word2Vec. Our findings indicate that SVM performs effectively with all three feature extraction methods, with TF-IDF yielding the highest accuracy at 0.79. Although the BoW method showed competitive results, it slightly trailed TF-IDF in k-fold validation. Word2Vec, however, exhibited the lowest performance, achieving a maximum accuracy of 0.69. A comparative analysis of accuracy, precision, recall, and F1-score highlight the superiority of TF-IDF in delivering consistent and accurate results. Further statistical analysis using ANOVA revealed no significant differences between the models across any of the evaluation metrics. Additionally, the evaluation was conducted under several scenarios, including tests on balanced and imbalanced datasets, varying dataset sizes, and different CCC parameter values for SVM. These scenarios provided deeper insights into the factors influencing the system's performance, reinforcing that TF-IDF combined with SVM remains the most effective approach in this study.
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.
Proliferative Diabetic Retinopathy Detection Using Convolutional Neural Network with Enhanced Retinal Image Sabilla, Wilda Imama; Hani'ah, Mamluatul; Ririd, Ariadi Retno Tri Hayati; Amalia, Astrifidha Rahma
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.
Co-Authors Adhisuwignjo, Supriatna Aflah, Darin Zahira Agus Zainal Arifin Agus Zainal Arifin Alif Akbar Fitrawan Alif Akbar Fitrawan, Alif Akbar Amalia, Astrifidha Rahma Ananta, Ahmadi Yuli Annisa Puspa Kirana Annisa Puspa Kirana Ariadi Retno Tri Hayati Ririd Arie Rachmad Syulistyo Arwin Datumaya Wahyudi Sumari Aryo Harto Aryo Harto 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 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