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All Journal Jurnal Teknologi Informasi dan Ilmu Komputer IJoICT (International Journal on Information and Communication Technology) Jurnal Teknologi dan Sistem Komputer Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) JITTER (Jurnal Ilmiah Teknologi Informasi Terapan) INTEK: Jurnal Penelitian Building of Informatics, Technology and Science Journal of Computer System and Informatics (JoSYC) Charity : Jurnal Pengabdian Masyarakat eProceedings of Engineering Jurnal Abdimas LOGIC: Jurnal Penelitian Informatika
Bambang Ari Wahyudi
Telkom University
Arts Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Mechanical Engineering Social Sciences

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Pembangunan Ensiklopedia Kosa Kata Al Qur’an Menggunakan Generalized Vector Space Model dan Semantics Relatedness Annisa Dian Muktiari; Moch. Arif Bijaksana; Bambang Ari Wahyudi
eProceedings of Engineering Vol 5, No 3 (2018): Desember 2018
Publisher : eProceedings of Engineering

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Abstract

Abstrak Al Qur’an merupakan kitab suci bagi umat Islam dan menjadi pedoman dan sumber hukum paling utama. Al Qur’an memiliki 30 Juz, 114 Surat dan 6236 ayat. Dalam Al Qur’an terdapat ayat disetiap juz-nya yang masih kurang dipahami bagi kebanyakan orang. Dibutuhkan kamus atau ensiklopedia yang dikhususkan untuk membahas arti kata dalam Al Qur’an untuk memperoleh informasi yang lebih lengkap dengan menggunakan pedoman buku tafsir. Salah satu cara untuk mengukur keterkaitan kata dari setiap potongan kata dalam Al Qur’an adalah dengan menggunakan Generalized Vector Space Model (GVSM). GVSM merupakan metode pengembangan dari Vector Space Model (VSM) yang menambahkan fungsi sense dan penilaian pada makna antar kata dalam dokumen. Nilai yang didapat merupakan nilai similarity yang akan menentukan relevannya suatu dokumen dengan query yang dimasukkan user. Dalam penelitian ini, dokumen yang dimaksud adalah paragraf. Dari hasil pengujian yang dilakukan, metode GVSM mendapatkan nilai similarity yang lebih tinggi dari metode Vector Space Model (VSM) dan Latent Semantics Analysist (LSA) . Dokumen yang relevan dengan query user akan menghasilkan nilai similarity diatas 0.50. Kata kunci : al qur’an, tafsir, generalized vector space model, similarity. Abstract The Qur'an is a holy book for Muslims and the most important guideline and source of law. The Qur'an has 30 Juz, 114 Letters and 6236 verses. In the Qur'an there is a verse in every juz it is still not understood for most people. It takes a dictionary or encyclopedia devoted to discussing the meaning of the word in the Qur'an for obtaining more complete information using the guidebook of the commentary. One of measure the interconnectedness of every word in the Qur'an is to use the Generalized Vector Space Model (GVSM). GVSM is a method of development of the Vector Space Model (VSM) that adds sense function and judgment to the meaning of inter-word in documents. The value obtained is a value of similarity that will be relevant to the document that the user requested. From the results of the tests, the GVSM method gets a higher similarity value than the Vector Space Model (VSM) and Latent Semantics Analyzer (LSA) methods. Relevant documents with the user query will produce a similarity value above 0.50. Keywords: al qur'an, tafseer, generalized vector space model, similarity.
Deteksi Kemiripan Bagian-bagian Terjemah Al-Qur’an dengan Menggunakan Metode Latent Semantic Analysis Ardhi Akmaludin Jadhira; Moch Arif Bijaksana; Bambang Ari Wahyudi
eProceedings of Engineering Vol 5, No 3 (2018): Desember 2018
Publisher : eProceedings of Engineering

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Abstrak Dalam kitab suci umat muslim, yaitu Al-Qur’an terdapat bagian-bagian terjemah yang memiliki kemiripan semantik antar halaman berbeda. Dalam memahami kemiripan semantik dan mengetahui keterkaitan bagian-bagian terjemah Al-Qur’an bukan sesuatu yang mudah dan cepat, kemiripan semantik dalam Al-Qur’an cukup sulit dimengerti karena maknanya yang sangat kompleks. Permasalahan yang akan diangkat dalam tugas akhir ini adalah bagaimana mengetahui nilai kemiripan semantik dari halaman terjemah Al-Qur’an dengan halaman-halaman yang lain. Dengan menerapkan metode latent semantic analysis yang dibantu dengan teknik singular value decomposition dan low rank approximation diharapkan dapat membantu dalam mencari pasangan-pasangan yang memiliki kemiripan semantik. Dalam mencari nilai kemiripan semantik latent semantic analysis menggunakan perhitungan cosine similarity. Dataset yang digunakan dalam penelitian ini adalah teks terjemah Al-Qur’an berbahasa Inggris, dengan keluaran sistem yaitu tingkat kemiripan dari dua buah atau lebih halaman yang dipasangkan. Dari hasil pengujian bahwa dengan menggunakan dimensi atau parameter Rank K yang maksimum didapatkan akurasi dan Fmeasure yaitu 100%. Jika semakin kecil dimensi atau parameter Rank K yang digunakan adalah minimum maka nilai kemiripan semantik akan semakin besar dan beragam serta semakin tidak relevan dengan dataset pasangan-pasangan halaman yang telah ditentukan. Kata kunci : Terjemahan Al-Qur’an, Latent Semantic Analysis. Cosine Similarity Abstract In the Muslim holy book, Al-Qur’an contains translation parts that have semantic similarities between different pages. In understanding the semantic similarities and knowing the relevance of the parts of the translation of Al-Qur’an is not something that is easy and fast, the semantic similarity in Al-Qur’an is quite difficult to understand because of its very complex meaning. The problem that will be raised in this final project is how to find out the semantic similarity value of the translation pages of Al-Qur’an with other pages. By applying the latent semantic analysis method, which is assisted by singular value decomposition techniques and low rank approximation, it is expected to help in finding pairs that have semantic similarities. In looking for semantic similarity values, latent semantic analysis uses cosine similarity calculations. The dataset used in this research is the translation of Al-Qur’an in English, with the output of the system that is the level of similarity of two or more pages that are paired. From the results of testing that by using the maximum dimension or parameter of Rank K, accuracy and F-measure are 100%. If the smaller dimensions or the Rank K parameters used are the minimum, the semantic similarity value will be even greater and more diverse and increasingly irrelevant to dataset of predefined page pairs. Keywords: Al-Qur’an Translation, Latent Semantic Analysis, Cosine Similarity
Analisis Pencocokan Nama Arab Terjemahan Bahasa Indonesia Menggunakan Soundex dan Levenshtein Distance Fauzan Ramadhan; Moch. Arif Bijaksana; Bambang Ari Wahyudi
eProceedings of Engineering Vol 5, No 3 (2018): Desember 2018
Publisher : eProceedings of Engineering

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Abstract

Cara seseorang dalam mengeja nama orang lain seringkali berbeda dengan orang yang lain. Padahal nama yang akan dieja adalah nama dari seseorang yang sama. Seperti nama ‘Aisyah’, kadang ada yang mengejanya dengan kata ‘Aisyah’, ‘Aisha’, ‘Aisah’, dan lain-lain. Pencarian nama periwayat pada saat ini baru sampai tahap ‘string matching’, sehingga ketika seseorang menggunakan ejaan nama yang berbeda, sistem tersebut tidak akan menampilkan hadits yang diriwayatkan oleh nama-nama yang dianggap mirip. Harapannya dengan sistem yang dibangun ini akan memperbaiki pengalaman pencarian nama dari periwayat hadits, sehingga sistem yang ada akan menampilkan hadits yang diriwayatkan oleh seseorang dimulai dari yang mempunyai kemiripan nama tertinggi sampai terendah dengan nama yang dicari. Penelitian ini menggunakan metode Soundex, lalu dilanjutkan dengan metode Levenshtein. Setelah itu, akan dilakukan penghitungan untuk menilai kinerja dari sistem ini menggunakan nilai precision, recall, f-measure dan akurasi. Soundex akan menentukan nama yang mempunyai kesamaan pengucapan suatu nama terhadap nama lain. Sedangkan Levenshtein akan memberikan nilai kemiripan dari nama yang sudah dipilih sebelumnya dengan nama yang dicari oleh pengguna. Nilai kinerja sistem didapat dari hasil yang diberikan dibandingkan dengan nilai yang ada pada gold standard. Dengan beberapa pengujian, sistem yang dibangun menggunakan Soundex dan Levenshtein ini mendapatkan nilai akurasi sebesar 99.95 persen. Kata kunci : Soundex, Levenshtein, precision, recall, f-measure, akurasiAbstract The way someone spells someone else’s name is often different from other people. Even though the name that will be spelled is the name of the same person. Like the name ’Aisha’, sometimes there are who spell it with the words ’Aisyah’, ’Aisha’, ’Aisah’, and others. The search for the name of the narrator at this time is only until the string matching stage, so when someone uses a different spelling of the name, the system will not display the hadith narrated by names that are considered similar. The hope with this system will improve the experience of searching the names of the hadith narrators, so that the existing system will display the hadith narrated by someone starting from the one having the highest to the lowest name with the name sought. This study used the Soundex method, then continued with the Levenshtein method. After that, it will be calculated to assess the performance of this system using precision, recall, f-measure and accuracy. Soundex will determine the name that has the same pronunciation as a name for another name. Whereas Levenshtein will give a similar value of the name that has been previously selected with the name that is searched by the user. The value of system performance is obtained from the results given compared to the values that are at the gold standard. From several tests, the system built using Soundex and Levenshtein has an accuracy value of 99.95 percent. Keywords: Soundex, Levenshtein, precision, recall, f-measure, accuracy
Predicting Forest Fire Hotspots with Carbon Emission Insights Using Random Forest and Gradient Boosting Regression irma palupi; bambang ari wahyudi; Naila AL Mamuda; Ayu Shabrina
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.v9i2.865

Abstract

This research paper focuses on predicting the dispersion of carbon emissions, a crucial indicator for identifying potential forest fire hotspots in the wooded regions of Sumatra Island, Indonesia. Forest fires, often triggered by extended periods of dry weather, result in significant environmental degradation, impacting both the ecosystem and the economy. Furthermore, health concerns arise from smoke inhalation, leading to respiratory problems. To achieve this predictive capability, we harnessed valuable datasets, including GFED4.1s for carbon emissions and ERA5 for historical climate indicators, spanning from 1998 to 2022. Employing supervised learning ensemble methods, specifically Random Forest Regression (RFR) and Gradient Boosting Regression (GBR), we sought to forecast carbon emissions. It is noteworthy that our predictions encompassed carbon emission values from 1998 to 2023, providing insights into recent trends. Our analysis showed that GBR did better than RFR in terms of evaluation metrics, with a root mean square error (RMSE) of 10.87 and a mean absolute error (MAE) of 2.91. This was done by carefully tuning the hyperparameters. Additionally, our study highlighted that precipitation, temperature, and humidity were the primary climate factors influencing carbon emission values.
Algoritma Reversible Data Hiding dalam Mengamankan Karya Seni Gambar Digital Sadewa, I Made Aditya Putra; Wahyudi, Bambang Ari; Palupi, Irma
INTEK: Jurnal Penelitian Vol 12 No 1 (2025): April 2025
Publisher : Politeknik Negeri Ujung Pandang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31963/intek.v12i1.5193

Abstract

In today's digital era, protecting digital artworks, particularly images, has become increasingly important to prevent copyright infringement and forgery. This paper proposes a novel method for embedding secret data into images using Reversible Data Hiding (RDH) techniques that leverage histogram shifting and random sub-blocks. The method is designed to maintain the visual integrity of the image while allowing the insertion of critical information, such as copyright metadata. The dataset used consists of 13 digital artworks sized 1280x720 pixels in PNG format, reflecting a diversity of textures and colors. Experimental results demonstrate that the proposed method achieves a high embedding capacity with PSNR values exceeding 37 dB, indicating excellent image quality post data insertion. Additionally, the method exhibits resilience against illegal modifications, with the ability to detect changes in images that have had data embedded. By integrating a PIN-based authentication system, the method enhances the security and integrity of the embedded information. This research significantly contributes to the field of digital artwork protection, offering an effective solution to preserve the authenticity and aesthetic value of images while enabling secure and reversible data insertion. The findings underscore the potential of RDH techniques in safeguarding sensitive information across various applications, ensuring that digital artworks can be both protected and enjoyed without compromising their quality.
Mental Health Sentiment Analysis on Twitter using Ensemble Learning Algorithm Aziz, Kemal; Wahyudi, Bambang Ari; Palupi, Irma
Building of Informatics, Technology and Science (BITS) Vol 7 No 2 (2025): September 2025
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i2.7763

Abstract

Mental health problems have become an important health issue around the world. Poor understanding as well as low mental health awareness contribute to mental health healing efforts. In particular, Social media is becoming a platform for people to convey feelings and emotions. A dataset of 20,000 English tweets, equally divided into 10,000 depressed and 10,000 non-depressed tweets, which were cleaned and processed using Term Frequency-Inverse Document Frequency (TF-IDF) for feature extraction. The method used in this sentiment analysis introduces an ensemble learning framework that combines Naïve Bayes, Support Vector Machine, and Random Forest classifiers, using majority voting for prediction. Each classifier was optimized using the best parameters, and the models were validated through 5-fold cross-validation. The experimental results show that Naïve Bayes with α = 1 achieved an accuracy of 76.23% while Random Forest with 5000 trees at 76.77%, and Support Vector Machine with a linear kernel at 75.32%. By combining these classifiers, the ensemble model reached the highest accuracy of 77.88%, demonstrating the effectiveness of combining multiple models to improve performance.
Reversible Data Hiding pada Gambar Digital dengan Sistem Otentikasi Terintegrasi Wahyudi, Bambang Ari; Palupi, Irma; Putranto, Muhammad Fadhlan
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 12 No 5: Oktober 2025
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jtiik.2025125

Abstract

Reversible Data Hiding (RDH) adalah salah satu metode yang efektif dalam steganografi, yang memungkinkan penyisipan informasi ke dalam media digital, seperti gambar, dengan kemampuan untuk memulihkan data asli sepenuhnya setelah informasi yang disisipkan diekstraksi. Dengan mengkombinasikan menggunakan Personal Identification Number (PIN), pemilik karya seni dapat menjalankan proses penyisipan data, ekstraksi data, serta validasi gambar. PIN berfungsi sebagai mekanisme keamanan yang membatasi akses hanya kepada pemilik yang sah, sehingga memastikan perlindungan data serta mencegah manipulasi atau akses tidak sah terhadap karya digital. Selain itu, PIN digunakan untuk menentukan lokasi piksel yang dapat dimodifikasi pada gambar. Dataset yang digunakan adalah sepuluh gambar digital dengan ukuran piksel 720 x 1280 yang terdiri dari lima gambar digitar berwarna dan lima gambar hitam putih. Hasil pengujian sistem yang dibuat menunjukan sistem yang dibangun bekerja dengan baik dimana gambar hasil ekstraksi berkualitas baik dengan perbedaan kecil yang mungkin terlihat namun umumnya tidak mengganggu dengan nilai PSNR gambar berwarna adalah 38.076 db dan rata-rata PSNR gambar hitam putih 50.58. Sedangkan untuk pengujian SSIM nilai pengujian rata-rata 0.98 yang berarti secara struktural kedua gambar sama. Kapasitas penyimpanan informasi untuk gambar berwarna sebesar 13.05% sedangkan untuk gambar hitam putih sebesar 66.88%.   Abstract Reversible Data Hiding (RDH) is an effective method in steganography that enables the embedding of information into digital media, such as images, with the ability to fully recover the original data once the embedded information has been extracted. By integrating the use of a Personal Identification Number (PIN), the owner of a work of art can carry out the processes of data embedding, data extraction, and image validation. The PIN functions as a security mechanism that restricts access exclusively to the rightful owner, thereby ensuring data protection and preventing unauthorized manipulation or access to the digital artwork. Additionally, the PIN is employed to identify specific pixel locations that can be altered within the image. The dataset used consists of ten digital images with a resolution of 720 x 1280 pixels, consisting of five colored images and five grayscale images The results of the system evaluation indicate that the extracted images maintain high quality, with minimal differences that are generally imperceptible where the PSNR for colored images is 38.076 dB, and the average PSNR for grayscale images is 50.58 dB. Meanwhile, SSIM testing yielded an average value of 0.98, signifying that the structural similarity between the original and extracted images is nearly identical. The information storage capacity for colored images was measured at 13.05%, whereas for grayscale images, it reached 66.88%.
Predicting Forest Fire Hotspots with Carbon Emission Insights Using Random Forest and Gradient Boosting Regression palupi, irma; wahyudi, bambang ari; AL Mamuda, Naila; Shabrina, Ayu
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.v9i2.865

Abstract

This research paper focuses on predicting the dispersion of carbon emissions, a crucial indicator for identifying potential forest fire hotspots in the wooded regions of Sumatra Island, Indonesia. Forest fires, often triggered by extended periods of dry weather, result in significant environmental degradation, impacting both the ecosystem and the economy. Furthermore, health concerns arise from smoke inhalation, leading to respiratory problems. To achieve this predictive capability, we harnessed valuable datasets, including GFED4.1s for carbon emissions and ERA5 for historical climate indicators, spanning from 1998 to 2022. Employing supervised learning ensemble methods, specifically Random Forest Regression (RFR) and Gradient Boosting Regression (GBR), we sought to forecast carbon emissions. It is noteworthy that our predictions encompassed carbon emission values from 1998 to 2023, providing insights into recent trends. Our analysis showed that GBR did better than RFR in terms of evaluation metrics, with a root mean square error (RMSE) of 10.87 and a mean absolute error (MAE) of 2.91. This was done by carefully tuning the hyperparameters. Additionally, our study highlighted that precipitation, temperature, and humidity were the primary climate factors influencing carbon emission values.
Efficient Queue Management System for Rumah Sehat Dokter Zoji Clinic in Cianjur Palupi, Irma; Khairi, Muhammad Daffa; Wahyudi, Bambang Ari
Jurnal Abdimas Vol. 29 No. 1 (2025): June 2025
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/eawwtv27

Abstract

This charity project aims to implement an efficient queue management system for Rumah Sehat Dokter Zoji Clinic, located in Cianjur, Indonesia. The clinic, known for its alternative and affordable healthcare services using a "pay as you wish" model, experiences high patient volumes—ranging from 300 to 500 daily on weekdays and up to 1,000 on Sundays. To address the growing demand and improve patient care, a mobile application was developed using Flutter with Firebase as the database to organize patient data and manage queue schedules effectively. The system prioritizes patients based on the severity of their conditions, categorizing them into Emergency, Severe, Moderate, and Mild levels. This prioritization ensures that those in urgent need receive prompt care, while others are integrated into the regular queue accordingly. The project also focuses on educating clinic staff and patients about the new system, recognizing varying levels of familiarity with technology. A hybrid approach to the queue system remains in place, accommodating those who prefer traditional methods. The implementation of this system is expected to streamline operations at the clinic, enhancing the overall efficiency and effectiveness of patient care delivery.
Prediksi Menggunakan Model Fuzzy Time Series Studi Kasus Curah Hujan di Kabupaten Bandung Prasetyo, Huda Rizky; Palupi, Irma; Wahyudi, Bambang Ari
LOGIC: Jurnal Penelitian Informatika Vol. 1 No. 1 (2023): September 2023
Publisher : Universitas Telkom

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25124/logic.v1i1.6405

Abstract

Hujan dapat menimbulkan bencana apabila terjadi secara terus menerus, hujan tersebut tentunya memiliki curah hujan yang tinggi dan dapat diprediksi dengan beberapa metode. Presipitasi total merupakan salah satu faktor iklim yang dapat mengindikasikan akumulasi air hujan di atas batas aman. Model time series merupakan salah satu metode yang tepat untuk memprediksi curah hujan, karena memungkinkan untuk dapat menangkap pola musiman pada curah hujan. Pada penelitian ini, prediksi curah hujan di Kabupaten Bandung dilakukan dengan menggunakan model fuzzy time-series. Model fuzzy time series didasarkan pada logika fuzzy dan digunakan untuk menangani ketidakpastian dan ketidakjelasan yang melekat pada data cuaca. Model deret waktu fuzzy menggunakan konsep logika fuzzy untuk menangani ketidakpastian dan ketidaktepatan data deret waktu. Dataset curah hujan lokasi Kabupaten Bandung yang diperoleh dari ERA 5 tahun 1978-2020, akan digunakan sebagai data latih untuk membangun model dan pengujian. Kemudian hasil prediksi dibandingkan dengan hasil yang diperoleh dari model SARIMA. Perbandingan tersebut menunjukkan bahwa model deret waktu fuzzy merupakan pendekatan yang menjanjikan untuk meramalkan curah hujan karena menghasilkan skor kesalahan yang lebih kecil dari pada hasil prediksi dengan model SARIMA.
Co-Authors Aditya Ramadhan AL Mamuda, Naila Annisa Dian Muktiari Ardhi Akmaludin Jadhira Ayu Shabrina Ayu Shabrina Aziz, Kemal Fauzan Ramadhan Fauzi Dzulfiqar Wibowo Fery Kun Widi Yudantyo Gunawan Gunawan Irma Palupi Jondri Jondri Khairi, Muhammad Daffa M Nurkamal F Moch Arif Bijaksana Muhammad Rizki Chairulloh Naila AL Mamuda Prasetyo, Huda Rizky Putranto, Muhammad Fadhlan Putu Harry Gunawan Sadewa, I Made Aditya Putra Tb Dzulfiqar Alhafidh Tjokorda Agung Budi Wirayuda Wahyu Kurniawan Wanda Darmawan