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All Journal TEKNIK INFORMATIKA Prosiding SNATIKA Vol 01 (2011) Jurnal Teknologi Informasi dan Ilmu Komputer JOIN (Jurnal Online Informatika) Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Technomedia Journal IJIIS: International Journal of Informatics and Information Systems Journal of Applied Data Sciences
Khodijah Hulliyah
Universitas Islam Negeri Syarif Hidayatullah
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Engineering Languange, Linguistic, Communication & Media

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Impelemantasi Algoritma Djikstra untuk Mendapatkan Jalur Tercepat dan Jalur Terpendek Hulliyah, Khodijah
Prosiding SNATIKA Vol 01 (2011) Vol 1
Publisher : Prosiding SNATIKA Vol 01 (2011)

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

Abstract

Kemacetan di Jakarta sudah menjadi pemandangan sehari-hari. Banyak langkah-langkah yang telah dilakukan oleh pemerintah untuk mengatasi kemacetan tersebut, seperti pembangunan flyover dan underpass, pengoperasian jalur busway, pemberlakuan jam tree in one dan sebagainya. Akan tetapi kemacetan tetap saja masih sering terjadi sampai saat ini. Oleh karena itu diperlukan peran aktif dari pengguna jalan sendiri untuk dapat mengatasi kemacetan tersebut. Salah satu cara yang paling efektif yaitu dengan mencari rute alternatif yang dapat dilalui. Beberapa penelitian sebelumnya melakukan penelitian hanya menggunakan parameter jarak tempuh. Oleh karena itu penelitian ini mencoba membuat sebuah sistem yang menggunakan algoritma Dijkstra yang dapat menemukan jalur tercepat dan terpendek dengan menyertakan faktor kecepatan dan waktu tempuh perjalanan. Pada pengembangan sistem ini penulis menggunakan metode spiral model. Sistem ini memberikan keluaran berupa jalur tercepat dan terpendek dari tempat asal menuju tempat tujuan yang diinputkan oleh pengguna. Jalur tercepat dan terpendek tersebut dilengkapi dengan total jarak tempuh, waktu tempuh serta kecepatan rata-rata. Kata kunci : Algoritma Dijkstra, Rute Tercepat, Rute Terpendek, Spiral Model
Q-Madaline: Madaline Based On Qubit Khodijah Hulliyah; Solikhun Solikhun
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 7 No 5 (2023): October 2023
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v7i5.5080

Abstract

This research focuses on developing the MADALINE algorithm using quantum computing. Quantum computing uses binary numbers 0 or 1 or a combination of 0 and 1. The main problem in this research is how to find other alternatives to the MADALINE algorithm to solve pattern recognition problems with a quantum computing approach. The data used in this study are heart failure data to predict whether a patient is at risk of death. The data source comes from KAGGLE, consisting of 299 data with 12 symptoms and one target, alive or dead. The result of this study is an alternative to the MADALINE algorithm that uses quantum computing. The precision of the test results with MADALINE with a learning rate of 0.1 = 100% with 2 epochs. The accuracy of the test results using a quantum approach with a learning rate of 0.1 is 85.71%. The results of this study can be an alternative to the MADALINE algorithm with a quantum computing approach, although it has not shown better accuracy than the classical MADALINE algorithm. More research is needed to produce better accuracy with larger data.
Predicting Airline Passenger Satisfaction with Classification Algorithms Hayadi, B.Herawan; Kim, Jin-Mook; Hulliyah, Khodijah; Sukmana, Husni Teja
International Journal of Informatics and Information Systems Vol 4, No 1: March 2021
Publisher : International Journal of Informatics and Information Systems

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/ijiis.v4i1.80

Abstract

Airline businesses around the world have been destroyed by Covid-19 as most international air travel has been banned. Almost all airlines around the world suffer losses, due to being prohibited from carrying out aviation transportation activities which are their biggest source of income. In fact, several airlines such as Thai Airways have filed for bankruptcy. Nonetheless, after the storm ends, demand for air travel is expected to spike as people return for holidays abroad. The research is aimed at analyzing the competition in the aviation industry and what factors are the keys to its success. This study uses several classification models such as KNN, Logistic Regression, Gaussian NB, Decision Trees and Random Forest which will later be compared. The results of this study get the Random Forest Algorithm using a threshold of 0.7 to get an accuracy of 99% and an important factor in getting customer satisfaction is the Inflight Wi-Fi Service.
Revolutionizing Digit Image Recognition: Pushing the Limits with Simple CNN and Challenging Image Augmentation Techniques on MNIST Hulliyah, Khodijah; Abu Bakar, Normi Sham Awang; Aripiyanto, Saepul; Khairani, Dewi
Journal of Applied Data Sciences Vol 4, No 3: SEPTEMBER 2023
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v4i3.104

Abstract

This study aims to apply Convolutional Neural Networks (CNN) and image augmentation techniques in digit recognition using the MNIST dataset. We built a CNN model and experimented with various image augmentation techniques to improve digit recognition accuracy. The results showed that the use of CNN with image augmentation techniques was effective in improving digit recognition performance. In the data collection stage, we used the MNIST dataset consisting of images of handwritten digits as training and testing data. After building the CNN model, we apply image augmentation techniques such as rotation, shift, and flipping to the training data to enrich the data variety and prevent overfitting. The evaluation results show that the CNN model that has been trained with image augmentation techniques produces significant accuracy, with a maximum accuracy of 99.81%. We also performed an ensemble of several CNN models and found that this approach increased the digit recognition accuracy to 99.79%. This research has the potential for further development. Recommendations for further research include exploring more specific and complex image augmentation techniques, as well as using more challenging datasets. In addition, future research may consider improvements to the CNN architecture used or combining it with other methods such as recurrent neural networks (RNN).
Sinyal Elektroensefalografi Untuk Deteksi Emosi Saat Mendengar Stimulus Pembacaan Al-Quran Menggunakan Wavelet Transform Hulliyah, Khodijah; Setianingrum, Anif Hanifa; Santoso, William
Technomedia Journal Vol 8 No 2 Special Issues (2023): Special Issue: Sistem Informasi Manajemen Dalam Menunjang Teknolog
Publisher : Pandawan Incorporation, Alphabet Incubator Universitas Raharja

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/tmj.v8i2SP.2060

Abstract

Mendengarkan suara membaca Al-Qur'an (Murottal) diketahui sering digunakan untuk membuat suasana terasa santai. Oleh karena itu, dalam penelitian ini, kami menyelidiki sejauh mana stimulasi suara murottal mempengaruhi penampilan gelombang alfa yang terlihat pada gelombang otak menggunakan detektor sinyal Electoencephalography (EEG). Menggunakan Transformasi Wavelet. Gelombang otak yang terdeteksi oleh sinyal EEG kemudian dianalisis untuk setiap fase gelombang pada frekuensi alfa (8-13 Hz) untuk melihat keadaan rileks. Kami merekam data gelombang EEG dalam 4 kondisi, yaitu kondisi tenang, kondisi tegang, dan keduanya dengan stimulus suara murottal. Setiap kondisi dilakukan masing-masing selama 2 menit. Suara murottal diambil secara acak untuk mendapatkan variasi data. Hasil klasifikasi menggunakan Recurrent Neural Network (RNN) menunjukkan bahwa t raining menggunakan n data ormal dengan tombak s mencapai akurasi 52% ~ 59%, Normal dengan m urottal n ormal menghasilkan nilai akurasi 55% ~ 56%, normal dengan tombak m urottal s mendapatkan nilai akurasi terkecil 35% ~ 46%, s Pike dengan m urrottal n ormal mencapai akurasi 57% ~ 67%, pike S dengan pike M urottal smenghasilkan akurasi 51% ~ 60%, M urottal normal dengan pike M urottal S mencapai nilai akurasi tertinggi 78%. Hal ini menunjukkan bahwa terdapat pengaruh yang signifikan dalam mendengarkan Murottal Al-Quran.
Improving Indonesian Named Entity Recognition for Domain Zakat Using Conditional Random Fields Widiyanti, Nur Febriana; Sukmana, Husni Teja; Hulliyah, Khodijah; Khairani, Dewi; Oh, Lee Kyung
JOIN (Jurnal Online Informatika) Vol 8 No 2 (2023)
Publisher : Department of Informatics, UIN Sunan Gunung Djati Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15575/join.v8i2.898

Abstract

In Indonesia, where the majority of the population is Muslim, one of the obligations of a Muslim is zakat. To reduce illiteracy about zakat among Muslims, they need to have access to basic information about it. In order to facilitate the acquisition of this information, this study utilized named entity recognition (NER) and defined 12 named entity classes for the zakat domain, including the pillars of Islam, various types of zakat, and zakat management institutions. The Conditional Random Fields method was used for testing Indonesian-NER in three scenarios. In the specific context of the Zakat domain, NER can extract information about organizations, individuals, and locations involved in collecting and distributing Zakat funds. This information can improve the Zakat system’s efficiency and transparency and support research and analysis on Zakat-related topics. The average performance evaluation of the Indonesian-NER model showed a precision of 0.902, recall of 0.834, and an F1-score of 0.867.
Feature Extraction Using Mel-Frequency Cepstral Coefficients (MfCC) Technique For A Tajweed Guess Based on Android Application Development Hulliyah, Khodijah; Kultsum, Lilik Ummi; Wibowo, Wahyu Hendarto; Setianingrum, Anif Hanifa; Arini, Arini; Durachman, Yusuf
JURNAL TEKNIK INFORMATIKA Vol. 18 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.v18i1.44721

Abstract

The development of information and communication technology today has had a significant impact on various aspects of life, including education. One notable example is the increasing number of applications designed for learning to recite the Quran with proper tartil. The growing trend of tahfidz (Quran memorization) is undoubtedly a positive development from a religious perspective. However, many individuals focus solely on memorization without acquiring the ability to recite the Quran properly and accurately. One discipline that supports proper Quran recitation is the knowledge of tajweed. Numerous applications have been developed in this field, especially on Android platforms. However, applications that utilize artificial intelligence (AI) to recognize tajweed rules and involve users in guessing tajweed readings are still in need of further development. The aim of this research is to develop a tajweed learning application using the concept of Automatic Speech Recognition (ASR). This study employs data collection methods such as literature review, quantitative methods, and testing. The design is represented using Unified Modeling Language (UML), while the application is tested using the Black Box Testing method. For data analysis and testing of the speech recognition model, the Hidden Markov Model (HMM) algorithm is employed, with Mel-Frequency Cepstral Coefficients (MFCC) used for feature extraction. The output of this research is an Android-based tajweed learning application that integrates speech recognition and allows users to guess tajweed rules interactively.
Pengembangan Plugin Read-Aloud Pada Moodle Menggunakan Model Transfer Learning Whisper Yusuf, Maulana; Hulliyah, Khodijah; Eka Muzayyana Agustin, Fenty; Hakiem, Nashrul; Marzuki Shofi, Imam
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 13 No 3: Juni 2026
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jtiik.2026133

Abstract

Keterampilan berbicara dalam Bahasa Inggris merupakan kompetensi penting bagi mahasiswa, namun praktik pembelajaran di ruang kelas masih menghadapi berbagai kendala seperti keterbatasan waktu, tingginya rasio pengajar-mahasiswa, serta ketiadaan umpan balik yang cepat dan juga konsisten. Pusat Pengembangan Bahasa (PPB) UIN Syarif Hidayatullah Jakarta telah menggunakan Learning Management System (LMS) berbasis Moodle sebagai platform pembelajaran dalam program mereka. Namun demikian, saat ini LMS tersebut belum memiliki fitur latihan dan evaluasi otomatis terhadap keterampilan berbicara mahasiswa. Penelitian ini bertujuan untuk mengembangkan plugin latihan read-aloud berbasis Automatic Speech Recognition (ASR) menggunakan Whisper yang terintegrasi penuh dengan LMS Moodle. Pengembangan dilakukan dengan metode Rapid Application Development (RAD) dengan pendekatan client-server, dimana Moodle berperan sebagai client untuk antarmuka pengguna, serta layanan backend berbasis FastAPI menangani pemrosesan audio, transkripsi serta perhitungan skor. Mekanisme penilaian dirancang secara heuristik untuk menghasilkan skor Accuracy, Pronunciation, dan Fluency beserta umpan balik otomatis. Pengujian fungsionalitas dilakukan melalui 20 skenario black-box testing serta pengujian integrasi dilakukan untuk memverifikasi konsistensi data antara backend dan Moodle. Hasil pengujian menunjukkan bahwa seluruh fungsionalitas sistem berjalan sesuai dengan spesifikasi dan integrasi sistem berlangsung stabil. Dengan demikian, plugin yang dikembangkan layak digunakan sebagai media latihan read-aloud  Bahasa Inggris mandiri yang terintegrasi dengan LMS Moodle.   Abstract Speaking skills in English are a crucial competency for university students. However, classroom-based learning practices still face obstacles such as limited practice time, high teacher-student ratios, and the lack of fast and consistent feedback. The Language Development Center (PPB) of UIN Syarif Hidayatullah Jakarta has utilized a Moodle-based Learning Management System (LMS) platform for its program. However, this LMS currently lacks a feature for the automatic practice and evaluation of student speaking skills. This study aims to develop an automatic read-aloud practice plugin based on Automatic Speech Recognition (ASR) Whisper, fully integrated with the Moodle LMS. The system was developed using the Rapid Application Development (RAD) method, with a client-server approach, with Moodle acting as the client for the user interface, while a FastAPI-based backend service handles audio processing, transcription, and score computation. The assessment mechanism is heuristically designed to generate Accuracy, Pronunciation, and Fluency scores along with automated feedback. Functional testing was conducted through 20 black-box test scenarios and integration testing was performed to verify data consistency between the backend service and Moodle. The test results indicate that all system functionalities run according to specifications and the integration process ran reliably. Therefore, the developed plugin is feasible for use as a self-directed English read-aloud practice tool, fully integrated within the Moodle LMS.
Co-Authors Abu Bakar, Normi Sham Awang Anif Hanifa Setianingrum, Anif Hanifa Arini Arini B. Herawan Hayadi Eka Muzayyana Agustin, Fenty Husni Teja Sukmana Imam Marzuki Shofi, Imam Khairani, Dewi Kim, Jin-Mook Kultsum, Lilik Ummi Maulana Yusuf Nashrul Hakiem Oh, Lee Kyung Saepul Aripiyanto Santoso, William Solikhun Solikhun Wibowo, Wahyu Hendarto Widiyanti, Nur Febriana Yusuf Abdurachman