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All Journal Teknika Jurnal CoreIT JIEET (Journal of Information Engineering and Educational Technology) INOVTEK Polbeng - Seri Informatika Sebatik Krea-TIF: Jurnal Teknik Informatika JOISIE (Journal Of Information Systems And Informatics Engineering) Building of Informatics, Technology and Science JTIM : Jurnal Teknologi Informasi dan Multimedia Jurnal Ilmu Komputer Jurnal Computer Science and Information Technology (CoSciTech) Bulletin of Computer Science Research KLIK: Kajian Ilmiah Informatika dan Komputer Malcom: Indonesian Journal of Machine Learning and Computer Science SATIN - Sains dan Teknologi Informasi Journal Of Artificial Intelligence And Software Engineering Jurnal Indonesia : Manajemen Informatika dan Komunikasi
Febi Yanto, Febi
UIN Suska Riau
Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering Languange, Linguistic, Communication & Media Library & Information Science

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Sistem Manajamen Risiko Keamanan Aset Teknologi Informasi Menggunakan ISO 31000:2018 Candra, Reski Mai; Sari, Yuli Novita; Iskandar, Iwan; Yanto, Febi
Jurnal CoreIT: Jurnal Hasil Penelitian Ilmu Komputer dan Teknologi Informasi Vol 5, No 1 (2019): Juni 2019
Publisher : Fakultas Sains dan Teknologi, Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (742.899 KB) | DOI: 10.24014/coreit.v5i1.8200

Abstract

DISKOMINFOPS Kabupaten Indragiri Hilir merupakan instansi yang menjadikan TI sebagai penggerak dalam keberlangsungan kinerja pemerintah. Disamping kesuksesan dalam merancang manajemen teknologi informasi, dibutuhkan juga manajemen risiko aset teknologi informasi pada DISKOMINFOPS Kabupaten Indragiri Hilir karena dinas tersebut belum menerapkan suatu kerangka kerja berbasis keamanan informasi dalam mengelola risiko aset teknologi informasi. Salah satu penyebabnya ialah kurangnya pemahaman pejabat teknologi informasi tentang manajemen keamanan terhadap aset teknologi informasi, sehingga memunculkan berbagai permasalahan, seperti perangkat keras di dinas tersebut masih banyak dalam keadaan tidak terawat dan rusak begitu saja tanpa adanya penanganan khusus. Dalam mewujudkan instansi pemerintah yang berbasis IT yang memiliki manajemen risiko yang baik, perlu menerapkan standar keamanan informasi yaitu ISO 31000:2018. Framework ini dapat memberikan prinsip dan pedoman yang generik pada manajemen risiko dengan konseptual. Penelitian ini bertujuan untuk membuat sistem manajemen keamanan risiko teknologi informasi yang ada di DISKOMINFOPS Kabupaten Indragiri Hilir menggunakan ISO 31000:2018.
Analisa dan Perbaikan Algoritma Line Maze Solving Untuk Jalur Loop, Lancip, dan Lengkung pada Robot Line Follower (LFR) Yanto, Febi; Welly, Irma
Jurnal CoreIT: Jurnal Hasil Penelitian Ilmu Komputer dan Teknologi Informasi Vol 1, No 2 (2015): Desember 2015
Publisher : Fakultas Sains dan Teknologi, Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (654.097 KB) | DOI: 10.24014/coreit.v1i2.1231

Abstract

Pengujian robot line follower pada penelitian ini menggunakan arduino uno. Pada penelitian sebelumnya algoritma line maze solving yang digunakan sebagai metode pada robot line follower masih menyisakan kendala, di mana robot belum bisa melewati maze dengan jalur loop, jalur lengkung lebih dari 225 derajat, dan jalur lancip kurang dari sudut 45 derajat. Oleh karena itu dilakukan perbaikan, dan didapatkan algoritma yang baru. Algoritma ini kemudian diuji kembali pada robot dan berhasil menyelesaikan maze dengan jalur loop, jalur lengkung lebih dari 225 derajat , dan jalur lancip kurang dari 45 derajat.
Performance Analysis of LVQ 1 Using Feature Selection Gain Ratio for Sex Classification in Forensic Anthropology Harni, Yulia; Afrianty, Iis; Sanjaya, Suwanto; Abdillah, Rahmad; Yanto, Febi; Syafria, Fadhilah
Building of Informatics, Technology and Science (BITS) Vol 5 No 1 (2023): June 2023
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v5i1.3625

Abstract

One approach to handling large of data dimensions is feature selection. Effective feature selection techniques produce the essential features and can improve classification algorithms. The accuracy performance results can measure the accuracy of the method used in the classification process. This research uses the Learning Vector Quantization (LVQ) 1 method combined with Gain Ratio feature selection. The data used is male and female skull bone measurement data totaling 2524. The highest accuracy results are obtained by LVQ 1, which uses a Gain Ratio with a threshold of 0.01 with a learning rate = 0.1, which is 92.01%, and the default threshold weka(-1.7976931348623157E308) with a learning rate = 0.1, which is 92.19%. In comparison, previous research that did not use gain ratio or that did not use GR only had the best results of 91.39% with a learning rate = 0.1, 0.4, 0.7, 0.9. This shows that LVQ 1 using the Gain Ratio can be recommended to improve the performance of the Skull dataset compared to LVQ 1 without Gain Ratio.
Steganografi Gambar Menggunakan Metode Least Significant Bit Pada Citra Dengan Operasi XOR Adha, Martin; Yanto, Febi; Handayani, Lestari; Pizaini, Pizaini
Building of Informatics, Technology and Science (BITS) Vol 6 No 1 (2024): June 2024
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v6i1.5262

Abstract

One way to secure secret messages from other unauthorized parties is steganography. One of the most widely used methods in steganography is Least Significant Bit. This research uses images as cover images and secret images. The image is resized to a resolution of 512x512 pixels, The cover image uses an RGB channel image and the secret image also uses an RGB channel image. In this research, LSB will be combined with triple XOR so that it can increase the security of this message hiding method. Triple XOR is used to provide extra security to images that have a secret image (Stego Image) inserted. In this research, several tests were also carried out, including testing the Peak Signal to Noise Ratio (PSNR) and Mean Square Error (MSE), for robustness testing it was also carried out by making modifications to the stego image such as resizing, compressing, and adding and reducing contrast. The results of this research's PSNR testing are very good, namely approximately 49 dB and lower MSE. With the PSNR and MSE results, it can be proven that the LSB method has a good level of imperceptibility. In experiments on image resistance to modification, several experimental results show that secret image extraction in the stego image failed to be extracted, and from several experiments such as adding and reducing contrast, image rotation and lossless compression, the image inserted in the stego image was successfully extracted.
Klasifikasi Kematangan Buah Mangga Menggunakan Pendekatan Deep Learning Dengan Arsitektur DenseNet-121 dan Augmentasi Data Permata, Rizkiya Indah; Yanto, Febi; Budianita, Elvia; Iskandar, Iwan; Syafria, Fadhilah
Building of Informatics, Technology and Science (BITS) Vol 6 No 1 (2024): June 2024
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v6i1.5381

Abstract

Mango is a seasonal fruit in Indonesia. In lowland areas and hot climates, this mango plant can grow abundantly. People who use mangoes generally focus more on the characteristics of the fruit which require a more precise classification to be more certain. Traditional classifications sometimes fail to properly articulate maturity criteria. This research classifies mango ripeness using a deep learning approach with densenet-121 architecture, parameters, learning rate, dropout, and data augmentation. Augmentation is the process of changing or modifying an image in such a way that the computer will detect that the image has been changed is the same picture. The original dataset was 895 data, after being augmented it became 1790 data consisting of three classes, namely ripe mango, young mango, and rotten mango. The test compares the original data and the original data added with augmentation. Accuracy using original data is 95.95%. Meanwhile, using original data combined with augmentation gets an accuracy of 99.73%
Penerapan Langchain Retriever dengan Model Chat Openai dalam Pengembangan Sistem Chatbot Hadis Berbasis Telegram Herwanza, Niken Aisyah Maharani; Harahap, Nazruddin Safaat; Yanto, Febi; Insani, Fitri
JTIM : Jurnal Teknologi Informasi dan Multimedia Vol 6 No 1 (2024): May
Publisher : Puslitbang Sekawan Institute Nusa Tenggara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35746/jtim.v6i1.514

Abstract

In Islamic studies, the Hadiths of Prophet Muhammad (SAW) hold significant value as guides for behavior and faith. However, access to understanding Hadiths often presents challenges, espe-cially for those who are not Hadith experts. The digitalization of Hadiths is still limited, making it time-consuming to find answers by sifting through the vast amount of available information. This research aims to create an efficient chatbot that provides answers related to Hadiths, including the original sources, quickly. The proposed solution is a technology-based approach through the development of a Hadith chatbot on Telegram, integrated with the LangChain Retriever and the GPT-4-1106-preview chat model from OpenAI. Using LangChain Retriever helps the chatbot find accurate answers by matching user questions with relevant Hadith databases, enhancing the ac-curacy of the chatbot's responses. The GPT-4-1106-preview chat model enables the chatbot to generate natural and context-appropriate responses, improving user interaction. The Rapid Ap-plication Development (RAD) method is applied in system development, through stages of Re-quirement Planning, User Design, Construction, and Cut-Over, including data analysis of Hadiths from the Nine Imam Hadith Books, totaling 62,169 Hadiths. The chatbot's performance evaluation uses the Scoring Evaluator framework with an average evaluation score of 0.97 and quality answer evaluation testing by five Hadith experts with an accuracy percentage of 90%. The Scoring Eval-uator test results indicate that the responses are highly accurate and aligned with Hadith refer-ences, and the quality answer evaluation test on a Likert scale shows respondents strongly agree with the system's answers. This research contributes to laypersons wanting to learn Hadiths by utilizing the chatbot as an interactive and innovative learning medium. Further research can expand the focus to complex interpretations of Musykil al-Hadith and asbab al-wurud to address deeper questions about Hadith interpretation.
Pengaruh Image Enhancement Contrast Stretching dalam Klasifikasi CT-Scan Tumor Ginjal menggunakan Deep Learning Yanto, Febi; Hatta, M Ilham; Afrianty, Iis; Afriyanti, Liza
Jurnal Inovtek Polbeng Seri Informatika Vol 9, No 1 (2024)
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/isi.v9i1.4233

Abstract

Kidney tumors are the third most common after prostate and bladder tumors, accounting for around 208,500 cases (2%) of all cancer cases globally. Renal Cell Carcinoma constitutes 85% of these cases, transitional cell cancer 12%, and other types 2%. In Indonesia, the incidence is 3 per 100,000 people, with a male-to-female ratio of 3.2:1. Ultrasound, CT scans, and MRI are used to detect, diagnose, and assess kidney tumors, with CT scans being crucial for evaluating complex lesions, both cystic and solid. This study uses the Image Enhancement Contrast Stretching technique to improve CT-Scan image quality for deep learning classification using the EfficientNet-B0 architecture. The dataset is split into training, validation, and testing sets in an 80:20 ratio. Hyperparameters include Adamax and RAdam optimizers with learning rates of 0.01, 0.001, and 0.0001. The highest performance was achieved using the Image Enhancement Contrast Stretching technique with the RAdam optimizer and a learning rate of 0.01, resulting in 100% accuracy, precision, recall, and F1-score. For the original dataset using the Adamax optimizer with a 0.01 learning rate, the highest performance was 99.12% accuracy, 98.28% precision, 100% recall, and 99.13% F1-score. This technique significantly enhances the performance of kidney tumor classification models.
Pengaruh Contrast Limited Adaptive Histogram Equlization dalam Klasifikasi CT-Scan Tumor Ginjal menggunakan Deep Learning Yanto, Febi; Jannata, Nanda; Handayani, Lestari; Cynthia, Eka Pandu
Jurnal Inovtek Polbeng Seri Informatika Vol 9, No 1 (2024)
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/isi.v9i1.4235

Abstract

The human excretory system, comprising the kidneys, ureters, and bladder, plays a crucial role in maintaining overall body health by filtering blood and eliminating waste products, including water and toxins. However, kidneys are susceptible to various diseases, such as kidney tumors, which present a significant global health challenge, with over 430,000 new cases reported in 2020. This research focuses on using CT-scan imaging techniques to analyze and assess kidney tumors. The study employs the Image Enhancement Contrast Limited Adaptive Histogram Equalization (CLAHE) method to enhance the quality of Kidney Tumor CT-Scan images for deep learning classification using the MobileNetV2 Architecture. The dataset, consisting of 4,560 images, is divided into training, validation, and testing sets in an 80:20 ratio. Applying CLAHE with a clip limit of 20 and an 8x8 tile grid significantly improves evaluation metrics compared to non-CLAHE datasets, achieving an impressive f1-score of 99.56% and accuracy of 99.56%. This improvement is achieved using the Adam optimizer with a learning rate of 0.01. These findings underscore the efficacy of CLAHE in enhancing the model's performance in kidney tumor classification. They are particularly valuable for radiologists as they enhance diagnostic accuracy and efficiency, potentially reducing diagnostic errors and improving patient outcomes.
Peringkas teks otomatis pada artikel berbahasa indonesia menggunakan metode maximum marginal relevance Idhafi, Zaky; Agustian, Surya; Yanto, Febi; Safaat H, Nazruddin
Computer Science and Information Technology Vol 4 No 3 (2023): Jurnal Computer Science and Information Technology (CoSciTech)
Publisher : Universitas Muhammadiyah Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37859/coscitech.v4i3.6311

Abstract

Peringkas teks otomatis atau automated text summarization adalah suatu metode untuk mengambil inti dari satu atau lebih dokumen teks. Peringkas Teks otomatis diperlukan untuk proses pembacaan, pencarian, dan pemahaman informasi menjadi lebih cepat dan efisien. Penelitian ini mengusulkan metode Maximum Marginal Relevance untuk melakukan proses peringkasan teks secara otomatis. Metode dikembangkan dan diuji pada masing-masing 150 dokumen artikel berbahasa Indonesia. Ringkasan dihasilkan dari skor kemiripan antar kalimat yang dihitung menggunakan cosine similarity. Performa MMR dalam menghasilkan ringkasan dievaluasi menggunakan ROUGE(Recall-Oriented Understudy for Gisting Evaluation), yang membandingkannya dengan ringkasan yang dibuat oleh manusia (gold standard). Hasil pengujian untuk tingkat kompresi 50%, memberikan nilai F1-score pada ROUGE-1, ROUGE-2, dan ROUGE-L masing-masing sebesar 71.86%, 64.18%, dan 71.56%. Sedangkan hasil pengujian dengan tingkat kompresi 30% menghasilkan F1-score untuk ROUGE-1, ROUGE-2, dan ROUGE-L masing-masing 62.95%, 53.61%, dan 62.47%. Dibandingkan penelitian terdahulu, penelitian ini menghasilkan skor yang lebih baik.
RANCANG BANGUN APLIKASI SIMULASI MINING PADA JARINGAN BLOCKCHAIN BITCOIN Sugandi, Hatami Karsa; Harahap, Nazruddin Safaat; Cynthia, Eka Pandu; Yanto, Febi; Sanjaya, Suwanto
Sebatik Vol. 26 No. 1 (2022): Juni 2022
Publisher : STMIK Widya Cipta Dharma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46984/sebatik.v26i1.1875

Abstract

Bitcoin merupakan salah satu dari mata uang digital yang dalam regulasinya tidak diatur oleh siapa pun seperti lembaga, organisasi maupun pemerintahan. Bitcoin menggunakan teknologi kriptografi atau yang biasa dikenal dengan teknologi Blockchain. Teknologi ini merupakan teknologi penyimpanan data atau transaksi kedalam sebuah block, dimana setiap proses penambahan block baru harus melalui proses validasi oleh sistem sesuai dengan konsensus yang berlaku. Untuk mengamankan jaringan Blockchain miliknya, bitcoin menggunakan algoritma konsensus Proof of Work (PoW). Proses validasi block inilah yang dinamakan dengan proses mining. Mining dilakukan untuk menambahkan transaksi kedalam Block dengan cara memecahkan teka-teki matematika dari algoritma PoW dengan cara memberikan komputasi power dari GPU oleh miner. Dikarenakan membutuhkan power yang besar, para miner diberi imbalan berupa bitcoin. Besaran bitcoin yang diterima tergantung dari hash power miner. Fenomena mining bitcoin menjadi trend bisnis pada masa kini karena menjanjikan keuntungan. Fenomena ini membuat banyak orang awam untuk ikut melakukan mining, tanpa mengetahui apa yang sebenarnya akan dilakukan. Maka dari itu simulasi ini dibuat dengan tujuan untuk mengedukasi bagaimana proses yang terjadi pada mining Bitcoin dengan cara visualisasi melalui Aplikasi web yang nantinya akan dibangun menggunakan bahasa pemrograman javascript dan diharapkan dapat menggambarkan proses mining pada blockchain dengan menerapkan algoritma konsensus Proof of Work di dalamnya.
Co-Authors Adha, Martin Afriyanti, Liza Afroni, Hallend Agustian, Surya Agustina, Auliyah Dewi, Nurika Eka Pandu Cynthia Eka Pandu Cynthia, Eka Pandu Elin Haerani Elvia Budianita Elvia Budianita Fadhilah Syafria Fadhilah Syafria Fauziyyah, Laila Nurul Fitri Insani Gusti, Gogor Putra Hafi Puja Gusti, Siska Kurnia Harni, Yulia Hatta, M Ilham Herwanza, Niken Aisyah Maharani Idhafi, Zaky Iis Afrianty Irma Welly, Irma Irsyad , Muhammad Iwan Iskandar Jannata, Nanda Lestari Handayani Masaugi, Fathan Fanrita Mazdavilaya, T Kaisyarendika Muhammad Fahri Muhammad Fikry Muhammad Irsyad Nabyl Alfahrez Ramadhan Amril Nazruddin Safaat H Negara, Benny Sukma Permata, Rizkiya Indah Pizaini Pizaini, Pizaini Prananda, Alga Putra, Wahyu Eka Rahmad Abdillah Rahman, Muhammad Taufikur Reski Mai Candra Shahira, Fayza Sofiyah, Wan Sugandi, Hatami Karsa Suwanto Sanjaya Suwanto Sanjaya Syafria, Fadhillah Wijaya, Andy Huang Wirdiani, Putri Syakira Yenggi Putra Dinata Yuli Novita Sari, Yuli Novita Yusra Yusra, Yusra