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All Journal Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) CommIT (Communication & Information Technology) Journal of ICT Research and Applications International Journal of Advances in Intelligent Informatics Scientific Journal of Informatics Journal of Information Systems Engineering and Business Intelligence Indonesian Journal on Computing (Indo-JC) IJoICT (International Journal on Information and Communication Technology) JOIV : International Journal on Informatics Visualization Sinkron : Jurnal dan Penelitian Teknik Informatika Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) International Journal of Artificial Intelligence Research Journal of Information Technology and Computer Science (JOINTECS) JURNAL MEDIA INFORMATIKA BUDIDARMA Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control JURIKOM (Jurnal Riset Komputer) Building of Informatics, Technology and Science Journal of Information Systems and Informatics RADIAL: JuRnal PerADaban SaIns RekAyasan dan TeknoLogi Indonesian Journal of Electrical Engineering and Computer Science Journal of Computer System and Informatics (JoSYC) Madani : Indonesian Journal of Civil Society Teknika Journal of Applied Data Sciences KLIK: Kajian Ilmiah Informatika dan Komputer Jurnal Abdimas Kartika Wijayakusuma Journal of Dinda : Data Science, Information Technology, and Data Analytics Jurnal Ilmiah IT CIDA : Diseminasi Teknologi Informasi SisInfo : Jurnal Sistem Informasi dan Informatika Jurnal INFOTEL RADIAL: Jurnal Peradaban Sains, Rekayasa dan Teknologi
Nur Ghaniaviyanto Ramadhan
Institut Teknologi Telkom Purwokerto
Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Energy Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Mathematics Mechanical Engineering Social Sciences Other

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Implementation of Neural Machine Translation for English-Sundanese Language using Long Short Term Memory (LSTM) Ramadhan, Teguh Ikhlas; Ramadhan, Nur Ghaniaviyanto; Supriatman, Agus
Building of Informatics, Technology and Science (BITS) Vol 4 No 3 (2022): December 2022
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v4i3.2614

Abstract

In this modern era, machine translation has been used all over the world for solving humankind’s problems such as it deals with language. Machine translation is almost used by people who want to translate their native language into their foreign language. The international language being used is the English language. Machine translation is the task to translate a source language to another language. The input of it is a word or a sentence from the source language and it will be translated into another language. The input of it is a word or a sentence from the source language and it will be translated into another language. There are many purposes for using machine translation such as learning another language, communicating, finding a certain or better word to use, and even writing something in a book or another article. Several methods have been conducted to do the machine translation task such as the statistical approach and the neural approach In terms of Sundanese machine translation, there are several methods or several approaches that other researchers have conducted. However the study about Sundanese machine translation, none of the research conducted the English into Sundanese language. In this study using the encoder and decoder LSTM architecture achieve a good result regarding building a model for machine translation task. The performance of this model has achieved 0.99 accuracies in both training and testing as well as less than 0.1 loss value to both training and testing data. This model also achieves more than 0.8 average BLEU score for both training and testing data.
Evaluasi User Experience pada Website Posyandu Menggunakan Metode In-Person Usability Testing dan User Experience Questionnaire (UEQ) Purwitasari, Rachma Wukir; Ramadhan, Nur Ghaniaviyanto
Jurnal Ilmiah IT CIDA Vol 9 No 2: Desember 2023
Publisher : STMIK AMIKOM Surakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55635/jic.v9i2.190

Abstract

Posyandu atau singkatan dari Pos Pelayanan Terpadu merupakan sistem yang berfungsi sebagai Upaya Kesehatan Bersumber Daya Masyarakat (UKBM) dengan masyarakat yang mengatur serta mengelola agar terciptanya pembangunan kesehatan yang baik, memudahkan masyarakat dalam memperoleh informasi perkembangan anak, dan mengurangi angka kematian bagi ibu dan anak. Pengolahan data posyandu Mawar 1 di Desa Karanglewas Kidul masih menggunakan cara manual yaitu dengan mencatat data anak-anak yang akan melakukan imunisasi di buku besar. Pencatatan tersebut menjadi salah satu masalah yang muncul bagi para petugas posyandu, karena dianggap kurang efektif dan butuh waktu lama mengolah data posyandu. Kemudian ada permasalahan pula yang muncul ketika seorang ibu yang lupa membawa buku posyandu dan ditakutkan datanya tidak relevan antara yang dikatakan oleh ibu tersebut dengan data yang ada di buku posyandu tersebut. Berdasarkan permasalahan di atas, sebuah sistem diperlukan guna memperlancar proses pengolahan data petugas posyandu dan memberikan sebuah informasi dengan segera dan menyeluruh untuk mempermudah serta mengurangi redundansi data dalam pengolahan data. Metode yang digunakan adalah In-person Usability Testing dan User Experience Questionnaire (UEQ). Hasil dari penelitian yang sudah dilakukan dengan 36 responden adalah rata-rata daya tarik sebesar 2,282, rata-rata kejelasan sebesar 2,188, rata-rata efisiensi sebesar 2,299, rata-rata ketepatan sebesar 2,215, rata-rata stimulasi sebesar 2,292, rata-rata kebaruan sebesar 2,181. Sehingga kesimpulan yang didapat setelah melakukan pengujian menggunakan UEQ adalah bahwa website posyandu berada pada level excellent.
Perancangan Aplikasi Monitoring Data Posyandu Mawar 1 Karanglewas Kidul Berbasis Website Menggunakan Metode User Experience Lifecyle Salsabila, Nisrinia Eka; Ramadhan, Nur Ghaniaviyanto
Jurnal Ilmiah IT CIDA Vol 9 No 1: Juni 2023
Publisher : STMIK AMIKOM Surakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55635/jic.v9i1.182

Abstract

Kemajuan teknologi yang semakin pesat mendukung perkembangan pertumbuhan penduduk semakin meningkat. Maka dari itu pengembangan ilmu teknologi ini mendorong manusia untuk menggunakan internet. Yang merupakan salah satu alat bantu manusia untuk mendapatkan sumber informasi dan penerapan sistem dalam pengelolaan sesuai dengan pelayanan yang di inginkan oleh masyarakat terutama dibidang kesehatan yaitu posyandu Pos Pelayanan Terpadu, adapun proses pencatatan dan pendataan masih manual dengan menggunakan buku besar. Berdasarkan permasalahan di atas, maka dapat diperlukan sebuah sistem berbasis website dikarenakan untuk mempermudah kader-kader yang bertugas di posyandu dalam mengolah data dan memberikan informasi dengan cepat dan mempermudah serta mengurangi keterlambatan data dalam pengelolaan data. Sistem ini menggunakan metode User Experience Life Cycle data menggunakan, Kuesioner Berdasarkan permasalahan yang ada, maka akan dilakukan penelitian tentang perancangan monitoring posyandu Mawar 1 berbasis website dengan menggunakan metode User Experience Lifecycle (UXL). Metode ini juga dapat menerapkan usability pada aplikasi berbasis website yang akan dirancang. Dimana pada usability testing perancangan prototype dilakukan pengujian usability dengan menerapkan metode SUS Tujuan penelitian ini yaitu untuk mengetahui tingkat kekurangan Ketika pengerjaan pengujian tersebut. Kebutuhan aplikasi berdasarkan pengalaman pengguna agar efektif dan efisien.
Analyzing risk factors and handling imbalanced data for predicting stroke risk using machine learning Adiwijaya, Adiwijaya; Ramadhan, Nur Ghaniaviyanto
International Journal of Advances in Intelligent Informatics Vol 11, No 1 (2025): February 2025
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v11i1.1678

Abstract

Stroke is a serious medical condition resulting from disturbances in blood flow to the brain, signaling a chronic health issue that requires an immediate response. Principal risk factors increasing the likelihood of stroke include the presence of pre-existing conditions such as Diabetes Mellitus (DM), hypertension, and high cholesterol levels. Effective preventive measures are crucial to minimize stroke risk, and using predictive methods based on data analysis from the clinical examination dataset over the last three years (2019-2021), known as the general checkup (GCU) dataset, presents an innovative approach. This study aims to predict an individual's stroke risk for the following year. In this context, the study also addresses the preprocessing stage of the GCU dataset, which includes solutions for missing values by substituting them with the statistical mean, label encoding, feature correlation analysis using entropy values, and addressing data imbalance with the Adaptive Synthetic (ADASYN) technique. To evaluate their predictive performance, the research involves comparisons among various machine learning models. The outcome of the experiment shows that the Random Forest model is the best model, with 98.7% accuracy and 63.9% F1-Score. This research highlights the importance of preemptive measures against stroke by utilizing predictive techniques on clinical data, with the Random Forest model proving most effective in forecasting stroke probability.
Enhancing SMOTE Using Euclidean Weighting for Imbalanced Classification Dataset Ramadhan, Nur Ghaniaviyanto; Maharani, Warih; Gozali, Alfian Akbar; Adiwijaya, Adiwijaya
Journal of Applied Data Sciences Vol 6, No 3: September 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i3.798

Abstract

Class imbalance is a significant challenge in machine learning classification tasks because it often causes models to be biased toward the majority class, resulting in poor detection of minority classes. This study proposes a novel enhancement to the Synthetic Minority Over-sampling Technique (SMOTE) by incorporating Euclidean distance-based feature weighting, called Weighted SMOTE. The key idea is to improve the quality of synthetic minority samples by calculating feature importance using a Random Forest model and assigning higher weights to the most relevant features. The objective of this research is to generate more representative synthetic data, reduce model bias, and increase predictive accuracy on highly imbalanced datasets. Experiments were conducted on four benchmark datasets from the KEEL Repository with imbalance ratios ranging from 0.013 to 0.081. The proposed Weighted SMOTE combined with an ensemble voting classifier (Random Forest, AdaBoost, and XGBoost) demonstrated significant improvements compared to standard SMOTE and models without resampling. For example, on the Zoo-3 dataset, the Balanced Accuracy Score (BAS) increased from 75% to 90%, while the F1-score improved from 48% to 94%. On the Cleveland-0_vs_4 dataset, precision improved from 83% to 91% and recall remained high at 99%. Statistical testing using the Wilcoxon signed-rank test confirmed these improvements with p-values 0.05 for key metrics. The findings show that the proposed method effectively balances sensitivity and precision, generates more meaningful synthetic samples, and reduces the risk of overfitting compared to conventional oversampling. The novelty of this work lies in integrating Euclidean-based feature weighting into the SMOTE process and validating its performance on multiple domains with varying feature types and imbalance ratios. These results indicate that the proposed Weighted SMOTE approach contributes a practical solution for improving classification performance and model stability on severely imbalanced data.
TEKNIK SMOTE DAN GINI SCORE DALAM KLASIFIKASI KANKER PAYUDARA Ramadhan, Nur Ghaniaviyanto; Adhinata, Faisal Dharma
RADIAL : Jurnal Peradaban Sains, Rekayasa dan Teknologi Vol. 9 No. 2 (2021): RADIAL: JuRnal PerADaban SaIns RekAyasan dan TeknoLogi
Publisher : Universitas Bina Taruna Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37971/radial.v9i2.229

Abstract

Breast cancer is a malignancy in breast tissue that can originate from the epithelium of the ducts and lobules. WHO says 30% - 50% of cancer cases can be prevented. Breast cancer prevention can be done utilizing screening or early diagnosis. The purpose of the initial diagnosis is that if a lump appears, predictions can be made whether it is classified as malignant or benign. Breast cancer prediction can be done using a dataset containing cancer-related parameters. However, sometimes the dataset used also has problems such as the amount of data is not balanced and the use of irrelevant features. This study aims to improve breast cancer prediction results by balancing the number of data classes and using the rank feature. The method used is SMOTE for imbalanced data and Gini score for rank features. The classification model used is random forest and naïve Bayes. The results obtained by the random forest classification model are superior to Naïve Bayes.
Peningkatan Kreativitas dan Keterampilan Digital Pemuda Karang Taruna Kampung Karasak Wibowo, Agung Toto; Fahlena, Hilda; Maharani, Warih; Ramadhan, Nur Ghaniaviyanto
Madani : Indonesian Journal of Civil Society Vol. 7 No. 2 (2025): Madani : Agustus 2025
Publisher : Politeknik Negeri Cilacap

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35970/madani.v7i2.2832

Abstract

Karasak Village, Ciheulang Village, Ciparay District, Bandung Regency, still faces challenges, including low digital literacy and limited use of information technology to support the development of the village's potential. This condition has implications for the limited ability of the younger generation to produce and distribute creative content that can strengthen local identity and increase village competitiveness in the digital realm. To address these challenges, the community service team implemented a training program targeting Youth Karang Taruna on December 15, 2024. The training materials were comprehensively designed, covering the use of social media, talent modules, storytelling, on-camera communication, video shooting techniques, and music and video editing. The method employed was a combination of theoretical instruction, direct practice, and interactive mentoring, enabling participants to produce digital content products independently. Evaluation of the activity was conducted through the distribution of questionnaires, with the results showing that 94% of participants agreed or strongly agreed with the usefulness of the training. These findings confirm that the activity was effective, well-received by participants, and has the potential to encourage increased digital literacy capacity and creativity of youth in creating content based on local potential, ready for publication on social media.
An Integrated Random Forest for Analyzing Public Sentiment on the “Makan Bergizi Gratis” Program Ramadhan, Nur Ghaniaviyanto; Khoirunnisa, Azka
Journal of Information System and Informatics Vol 7 No 3 (2025): September
Publisher : Universitas Bina Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51519/journalisi.v7i3.1184

Abstract

The “Makan Bergizi Gratis” (MBG) Program is a public policy aimed at improving the nutritional quality of the community, particularly vulnerable groups. However, the success of this program is heavily influenced by public sentiment and perception. This research analyzes public sentiment toward the MBG program thru the social media platform X using an ensemble-based machine learning approach. The proposed framework integrates the Random Forest algorithm and compares it with four other ensemble models: AdaBoost, XGBoost, Bagging, and Stacking. A total of 3,417 tweets were analyzed using the TF-IDF method, both with and without stemming. The Random Forest model showed the best performance with an accuracy of 91.15% and an ROC-AUC of 95.46% on the data without stemming, consistently outperforming the other models. Additionally, a visual analysis of word frequency provides a strong indication of public opinion. These findings demonstrate the effectiveness of Random Forest in managing unstructured sentiment data and provide valuable insights for policymakers to monitor public responses and improve program implementation with greater precision.
XGBoost for Predicting Airline Customer Satisfaction Based on Computational Efficient Questionnaire Nur Ghaniaviyanto Ramadhan; Aji Gautama Putrada
International Journal on Information and Communication Technology (IJoICT) Vol. 9 No. 2 (2023): Vol.9 No. 2 Dec 2023
Publisher : School of Computing, Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21108/ijoict.v9i2.864

Abstract

Customer satisfaction can be created through a well-crafted service quality strategy, which forms the cornerstone of a successful business-customer relationship. Establishing and nurturing these relationships with customers is vital for long-term success. Within the airline industry, a persistent challenge lies in enhancing the passenger experience during flights, necessitating a comprehensive understanding of customer demands. Addressing this challenge is crucial for airlines aspiring to thrive in a competitive landscape, thus underlining the significance of providing top-notch services. This study addresses this issue by leveraging predictive airline customer satisfaction data analysis. We forecast customer satisfaction levels using a powerful Extreme Gradient Boosting (XGBoost) ensemble-based model. An integral aspect of our methodology involves handling missing values in the dataset, for which we utilize mean-value imputation. Furthermore, we introduce a novel logistic Pearson Gini (Log-PG) score to identify the factors that significantly influence airline customer satisfaction. In our predictive model, we achieved notable results, showing an accuracy and precision of 0.96. To ascertain the efficiency of our model, we conducted a comparative analysis with other boosting-type ensemble prediction models, such as gradient boosting and adaptive boosting (AdaBoost). The comparative assessment established the superiority of the XGBoost model in predicting airline customer satisfaction.
TEKNIK SMOTE DAN GINI SCORE DALAM KLASIFIKASI KANKER PAYUDARA Ramadhan, Nur Ghaniaviyanto; Adhinata, Faisal Dharma
RADIAL : Jurnal Peradaban Sains, Rekayasa dan Teknologi Vol. 9 No. 2 (2021): RADIAL: JuRnal PerADaban SaIns RekAyasan dan TeknoLogi
Publisher : Universitas Bina Taruna Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (453.223 KB) | DOI: 10.37971/radial.v9i2.229

Abstract

Breast cancer is a malignancy in breast tissue that can originate from the epithelium of the ducts and lobules. WHO says 30% - 50% of cancer cases can be prevented. Breast cancer prevention can be done utilizing screening or early diagnosis. The purpose of the initial diagnosis is that if a lump appears, predictions can be made whether it is classified as malignant or benign. Breast cancer prediction can be done using a dataset containing cancer-related parameters. However, sometimes the dataset used also has problems such as the amount of data is not balanced and the use of irrelevant features. This study aims to improve breast cancer prediction results by balancing the number of data classes and using the rank feature. The method used is SMOTE for imbalanced data and Gini score for rank features. The classification model used is random forest and naïve Bayes. The results obtained by the random forest classification model are superior to Naïve Bayes.
Co-Authors Abdurrahman Ibnul Rasidi Adil El-Faruqi Adiwijaya Agung Toto Wibowo Agus Supriatman Aji Gautama Putrada Alfian Akbar Gozali Alon Jala Tirta Segara Ananda Aulia Rizky Arief Rais Bahtiar Arif Amrulloh Azka Khoirunnisa Bayu Erfianto Christoph Quix Diovianto Putra Rakhmadani Diva Angelica Enjeli Cistia Sukmawati Faisal Dharma Adhinata Fauzi, Muhammad Dzulfikar Hashimoto, Takako Hilda Fahlena Imelda Atastina Kemas Muslim Lhaksmana Kridabayu, Adam Nur Kurnianingsih Kurnianingsih, Kurnianingsih Lintang Suryaningrum Maman Abdurohman Merlinda Wibowo Mohammad Fikri Nur Syahbani Nabilah Sharfina Nia Annisa Ferani Tanjung Nicolaus Euclides Wahyu Nugroho Novi Rahmawati Novi Rahmawati Nugraha, Narantyo Maulana Adhi Nungki Selviandro Nur Fatin Liyana Mohd Rosely Purnama Dileon Yamora Nainggolan Purwitasari, Rachma Wukir Rachma Wukir Purwitasari Rahardian, Reva Ramadhan, Teguh Ikhlas Rifqi Akmal Saputra Rifqi Alfinnur Charisma Salsabila, Nisrinia Eka Sayyid Yakan Khomsi Pane Sigit Pramono Sofiyudin Pamungkas Warih Maharani Wibowo, Merlinda Yohani Setiya Rafika Nur