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All Journal Jurnal Rekayasa Hijau MIND (Multimedia Artificial Intelligent Networking Database) Journal Systemic: Information System and Informatics Journal REKA ELKOMIKA: Jurnal Pengabdian kepada Masyarakat Computing and Education Technology Journal
Kurnia Ramadhan Putra
Institut Teknologi Nasional Bandung
Agriculture, Biological Sciences & Forestry Computer Science & IT Control & Systems Engineering Earth & Planetary Sciences Electrical & Electronics Engineering Industrial & Manufacturing Engineering Other

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Sistem Pembelajaran Hukum Baca Al-Qur’an Menggunakan Algoritma LPC dan KNN Hafizh Achmad Dinan; Youllia Indrawaty N; Kurnia Ramadhan Putra
Systemic: Information System and Informatics Journal Vol. 6 No. 1 (2020): Agustus
Publisher : Program Studi Sistem Informasi Fakultas Sains dan Teknologi, UIN Sunan Ampel Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29080/systemic.v6i1.927

Abstract

A Muslim must be able to read the verses of the Qur’an properly as taught by the Prophet, Muhammad. Reading the Qur'an in accordance with tajwid is obligatory for every Muslim, if someone reads the Qur'an without using tajwid, the law is sinful. The development of the application of learning the Tajwid of Qur’an is aimed at helping a Muslim to be good at reading the Qur’an that is good and right.Al-Fatihah is uses in this application. Learning the Tajwid of Qur’an Application is using Linear Predictive Coding (LPC) method as sound feature extraction and K-Nearest Neigbor as matching with training data. For testing the pronunciation of the 1st verse obtained data accuracy of 83.3%, the 2nd verse is 86.7%, the 3rd verse is 85%, the 4th verse is 80%, the 5th verse is 88.3%, the 6th verse is 93.3%.
Pengenalan Karakter Huruf Braille dengan Metode Convolutional Neural Network Muhammad Fahmi Herlambang; Asep Nana Hermana; Kurnia Ramadhan Putra
Systemic: Information System and Informatics Journal Vol. 6 No. 2 (2020): Desember
Publisher : Program Studi Sistem Informasi Fakultas Sains dan Teknologi, UIN Sunan Ampel Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29080/systemic.v6i2.969

Abstract

Braille characters consists of 6 dots that are designed in such way to be a writing system to help blind people. However, learning or reading Braille characters isn’t an easy thing to do, because fingers sensitivity and understanding the writing system are needed to be able to read Braille. Therefore, there are some researches on Braille characters recognition with different methods and technologies, such as deep learning. The Convolutional Neural Network (CNN) is used. CNN method has been used in various recognition researches, such as face recognition, document analysis, image classification, etc. In this research, the CNN method is used to perform Braille characters recognition. The system performs the Braille character recognition process per character based on a model that has been trained using a dataset with the 26 Braille characters. The result of 81.54% accuracy is achieved for Braille character image acquisition with a smartphone with 0 to 4 degrees tilting and 30cm distance with training model using learning rate of 0.0001 and Adam optimizer.
Low-Code Platform for Health Protocols Implementation in Sabilussalam Mosque During The COVID-19 Pandemic Sofia Umaroh; Kurnia Ramadhan Putra; nur Fitrianti; Mira Musrini Barmawi
REKA ELKOMIKA: Jurnal Pengabdian kepada Masyarakat Vol 3, No 2 (2022): REKA ELKOMIKA
Publisher : Institut Teknologi Nasional, Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26760/rekaelkomika.v3i2.96-105

Abstract

The COVID-19 pandemic condition has changed various aspects of life, especially Muslims’ prayer activities. COVID-19 pandemic has especially affected mosques as a place for Muslims to pray five times a day and to have Friday prayer congregation. To prevent the spread of COVID-19, the Sabilussalam Mosque located at Jl. Dr. Hatta Bandung has restricted its capacity and ensured visitors’ body temperature below 37,4 C manually. However, physical contact still occured, and its capacity still exceeded 50%. Therefore, the adoption of self-check temperature and automatic capacity counter as a method of mitigating the COVID-19 pandemic in mosques was needed. This community service aims at implementing a system for controlling mosque capacity and for avoiding physical contact during praying. The Low Code-based app counts temperature data broadcasted by K3 Pro and stored in NowDB cloud-service. As a result, the system manages to control the mosque's capacity to a maximum of 50% without any physical contact because it relies on internet connection.
Cromosom: Aplikasi Crowdsourced Software Engineering Menggunakan Komponen Rekomendasi Task Berbasis Media Sosial Kurnia Ramadhan Putra; Muhammad Zuhri Catur Candra
Rekayasa Hijau : Jurnal Teknologi Ramah Lingkungan Vol 3, No 3 (2019)
Publisher : Institut Teknologi Nasional, Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26760/jrh.v3i3.3432

Abstract

ABSTRAK Konsep crowdsourcing dapat dimanfaatkan pada bidang rekayasa perangkat lunak yang dikenal dengan Crowdsourced Software Engineering (CSE). CSE digunakan untuk menyelesaikan task yang berkaitan dengan perangkat lunak, seperti desain, implementasi, dan pengujian perangkat luna serta perbaikan bug. Saat ini, permasalahan umum yang terjadi aplikasi CSE adalah worker menghabiskan waktu untuk menemukan task yang relevan sesuai dengan keahliannya dan requester sulit untuk memilih worker yang dapat dipercaya untuk mengerjakan task. Komponen sistem rekomendasi dapat diintegrasikan pada aplikasi CSE yang dipercaya mampu untuk mengatasi permasalahan tersebut. Beberapa penelitian yang ada tentang integrasi komponen sistem rekomendasi pada aplikasi CSE hanya untuk menangani rekomendasi task dan tidak memepertimbangkan trustworthiness dari worker yang akan mengerjakan task. Sedangkan pada penelitian ini, integrasi komponen sistem rekomendasi selain dapat membantu merekomendasikan task kepada worker juga melakukan perangkingan terhadap worker berdasarkan trustworthiness dari worker tersebut sehingga dapat menjadi pertimbangan untuk requester dalam memilih worker yang akan mengerjakan task. Pendekatan yang diusulkan pada penelitian ini adalah kombinasi antara pendekatan content based dengan individual based. Pendekatan content based untuk menangani proses pencocokan antara kebutuhan keahlian yang diperlukan untuk mengerjakan task dengan kualifikasi worker yang akan mengerjakan task. Sedangkan pendekatan individual based untuk menangani proses perhitungan nilai social profile dalam menghasilkan trustworthiness dari worker. Implementasi dilakukan dengan mengembangkan aplikasi CSE yang dikenal dengan Cromosom, yang diintegrasikan dengan komponen sistem rekomendasi untuk membantu merekomendasikan task kepada worker dan melakukan perangkingan terhadap worker berdasarkan trustworthiness dari worker tersebut. Dari hasil pengujian fungsionalitas yang dilakukan, aplikasi Cromosom dapat membantu worker untuk menemukan task yang lebih relevan sesuai dengan keahliannya, dan membantu requester dalam memilih worker yang memiliki trustworthiness untuk mengerjakan task. Kata kunci: crowdsourcing, crowdsourced software engineering, task recommendation. ABSTRACT The concept of crowdsourcing can be utilized in the field of software engineering known as Crowdsourced Software Engineering (CSE). CSE is used to address tasks related to software, such as design, implementation, and testing of software and bug fixes. Currently, a common problem that occurs with CSE applications is that workers spend time finding relevant tasks according to their expertise and requester is difficult to choose workers who can be trusted to do the task. The recommendation system component can be integrated into CSE applications that are believed to be able to overcome these problems. Some existing research on the integration of recommendation system components in CSE applications is only to handle task recommendations and not consider the trustworthiness of workers who will be working on tasks. Whereas in this study, the integration of recommendation system components in addition to being able to help recommend tasks to workers also rank workers based on the trustworthiness of the workers so that they can be considered for the requester in choosing workers who will do the task. The approach proposed in this study is a combination of content-based and individual-based approaches. Content-based approach to handle the matching process between the skill requirements needed to do the task and the qualifications of the worker who will be working on the task. While the individual-based approach to handle the process of calculating the value of social profiles in generating trustworthiness from workers. Implementation is done by developing a CSE application known as a Cromosom, which is integrated with the recommendation system component to help recommend tasks to workers and rank workers based on the trustworthiness of the worker. From the results of the functionality testing, the Cromosom application can help workers find more relevant tasks according to their expertise, and help requester in choosing workers who have trustworthiness to do the task. Keywords: crowdsourcing, crowdsourced software engineering, task recommendation.
RESULTANT: Data Preparation Techniques to Improve XGBoost Algorithm Performance KURNIA RAMADHAN PUTRA; SOFIA UMAROH; NUR FITRIANTI; SATRIA NUGRAHA
MIND (Multimedia Artificial Intelligent Networking Database) Journal Vol 8, No 1 (2023): MIND Journal
Publisher : Institut Teknologi Nasional, Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26760/mindjournal.v8i1.42-51

Abstract

ABSTRAKPrediksi credit scoring saat ini banyak digunakan dalam layanan peer-to-peer lending oleh perusahaan teknologi finansial. Salah satu teknologi yang digunakan untuk credit scoring adalah data mining menggunakan algoritma machine learning XGBoost yang memiliki tingkat akurasi yang tinggi. RESULTANT diusulkan sebagai teknik yang digunakan untuk memaksimalkan hasil dari salah satu tahapan data mining yaitu preparasi data. Dataset yang digunakan adalah data Lending Club dengan total 2.260.701 record dan 151 variabel. Tahapan yang dilakukan pada RESULTANT adalah seleksi fitur, penanganan missing value, penanganan data outlier dan penanganan data ketidakseimbangan. Dari tahap RESULTANT, dihasilkan 44 variabel akhir yang siap digunakan untuk membangun model dengan menggunakan algoritma XGBoost. Hasil menunjukkan bahwa RESULTANT mampu meningkatkan performa algoritma XGBoost dengan akurasi 99,17%, presisi 99,28%, recall 99,05%, spesifisitas 99,29%, ROC/AUC 99,94%, dan skor f1 99,17%.Kata kunci: XGBoost, Preparasi Data, Seleksi Fitur, Missing Value, OutlierABSTRACTCredit scoring predictions are currently widely used in peer-to-peer lending services by financial technology companies. One of the technologies used for credit scoring is data mining using the XGBoost machine learning algorithm which has a high degree of accuracy. We present RESULTANT as a technique used to maximize the results of one of the stages of data mining, namely data preparation. The dataset used is Lending Club data with a total of 2,260,701 records and 151 variables. The stages carried out in RESULTANT are feature selection, handling missing values, handling outlier data and handling imbalance data. From the RESULTANT stage, 44 final variables are produced which are ready to be used to build models using the XGBoost algorithm. The results showed that RESULTANT was able to improve the performance of the XGBoost algorithm with accuracy 99,17%, precision 99,28%, recall 99,05%, specificity 99,29%, ROC/AUC 99.94%, and f1-score 99,17%.Keywords: XGBoost, Data Preparation, Feature Selection, Missing Value, Outlier
VISUALISASI STRUKTUR AYAT AL-QUR’AN MENGGUNAKAN GRAPH DATABASE Kurnia Ramadhan Putra
Computing and Education Technology Journal Vol 3, No 2 (2023): OKTOBER
Publisher : Pendidikan Komputer FKIP Universitas Lambung Mangkurat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20527/cetj.v3i2.10466

Abstract

Kitab suci Al-Qur'an terdiri dari 114 surah dengan 6236 ayat yang dijadikan sebagai sumber ajaran dan pedoman untuk umat muslim di seluruh dunia. Semua ayat di dalam Al-Qur’an memiliki keterkaitan satu sama lain yang tersusun secara hierarki yaitu dalam sebuah surah terdiri dari nomor surah dan nama surah, kemudian setiap surah tersebut memiliki banyak ayat. Untuk melihat keterkaitan antar ayat dalam Al-Qur’an tersebut Penyimpanan data secara hierarki pada database relasional membutuhkan lebih banyak tabel dan terlalu banyak join tabel yang dilakukan yang membuat kinerja temu balik informasi menjadi lebih lambat. Oleh karena itu dibutuhkan graph database yang dapat menangani permasalahan tersebut yang mampu menyimpan data hierarki lebih efisien. Selain itu, graph database dapat membantu visualisasi keterkaitan antar ayat di dalam Al-Qur’an. Hasil penelitian ini menunjukkan bahwa graph database sebagai basis data non-relasional mampu menyimpan data hierarki Al-Qur’an dalam bentuk graf yang terdiri dari nodes untuk merepresentasikan surah dan ayat dan edges untuk merepresentasikan relasi atau hubungan antara surah dengan ayat. Hasil lainnya bahwa graph database mampu memvisualisasikan keterkaitan antar surah dengan ayat dengan lebih efisien tanpa membutuhkan kueri data dengan join tabel yang rumit.
Co-Authors Asep Nana Hermana Barmawi, Mira Musrini Hafizh Achmad Dinan Muhammad Fahmi Herlambang Muhammad Zuhri Catur Candra nur Fitrianti Nurhasanah, Youllia Indrawaty SATRIA NUGRAHA Sofia Umaroh