Article Per Year (5 Year)
p-Index From 2021 - 2026
2.693
P-Index
This Author published in this journals
All Journal CESS (Journal of Computer Engineering, System and Science) Jurnal Teknologi Elektro Sistemasi: Jurnal Sistem Informasi Jurnal Teknik Komputer AMIK BSI JITK (Jurnal Ilmu Pengetahuan dan Komputer) J I M P - Jurnal Informatika Merdeka Pasuruan Komputasi: Jurnal Ilmiah Ilmu Komputer dan Matematika Jurnal Mantik INTI Nusa Mandiri Jurnal Sistem Informasi Journal of Information System and Technology (JOINT) Jurnal Mandiri IT Teknologika International Journal of Emerging Issues in Islamic Studies Innovative: Journal Of Social Science Research Jurnal Teknoinfo
Indra Chaidir, Indra
Universitas Bina Sarana Informatika
Religion Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Mechanical Engineering Public Health Social Sciences Other

Published : 24 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 24 Documents
Search

 1   2   3 
KOMPARASI ALGORITMA K-NEAREST NEIGHBOR, SUPPORT VECTOR MACHINE, DAN NEURAL NETWORK UNTUK KLASIFIKASI PENYAKIT DAUN JERUK Deny Kurniawan; Dedi Triyanto; Mochamad Wahyudi; Lise Pujiastuti; Sumanto Sumanto; indra Chaidir
Jurnal Teknoinfo Vol. 19 No. 2 (2025): July 2025 Period
Publisher : Universitas Teknokrat Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33365/teknoinfo.v19i2.751

Abstract

Jeruk merupakan salah satu buah tropis yang banyak dikonsumsi masyarakat karena kandungan nutrisinya yang tinggi, khususnya vitamin C. Namun, produksi jeruk kerap mengalami penurunan akibat serangan penyakit, terutama pada bagian daun. Identifikasi penyakit secara manual dinilai kurang efisien dan rawan kesalahan, sehingga diperlukan sistem otomatis berbasis machine learning untuk membantu proses deteksi secara cepat dan akurat. Penelitian ini bertujuan untuk membandingkan tiga algoritma klasifikasi K-Nearest Neighbor (KNN), Support Vector Machine (SVM), dan Neural Network (NN) dalam mengidentifikasi penyakit daun jeruk berdasarkan fitur tekstur. Dataset yang digunakan terdiri dari lima kategori: Black Spot, Canker, Greening, Melanose, dan Healthy, dengan total 609 citra daun yang dibagi secara proporsional untuk pelatihan dan pengujian. Hasil evaluasi menunjukkan bahwa model Neural Network memberikan performa terbaik dengan akurasi 87,5%, diikuti oleh SVM sebesar 82,4%, dan KNN sebesar 77,5%. Penelitian ini menunjukkan bahwa pendekatan machine learning, khususnya Neural Network, efektif dalam klasifikasi penyakit daun jeruk dan berpotensi untuk diimplementasikan lebih lanjut dalam bentuk aplikasi praktis bagi petani.
The First Android Based Sharia Fintech Innovation in Indonesia to Increase Inclusive and Literate on Society’s Finance Suhartono Suhartono; Juniato Sidauruk; Octa Pratama Putra; Syamsul Bahri; Martias Martias; Aan `Rahman; Abdul Hamid; Lukman Hakim; Indria Widyastuti; Badurrachman Abdurrachman; Ninuk Riesmiyantiningtias; Rizky Amalia; Indra Chaidir
International Journal of Emerging Issues in Islamic Studies Vol. 1 No. 2 (2021): December 2021
Publisher : Research Synergy Foundation

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31098/ijeiis.v1i2.703

Abstract

Technology has become the part of today’s people life. Then, it is actually close to the application of it. Absolutely, it has example; such as the electricity for having more sophisticated in financial technology (Fin-Tech). The simplicity and speed of this technology have led people to adopt it in everyday’s life. One of the innovations in developing business and the economy, especially in the banking sector, is currently to develop Fintech (Financial Technology) which is able to facilitate all types of buying and selling transactions, investments and fundraising. Next, the purpose of this study is to explain and provide an understanding of the technical, procedures and benefits of the application, it is called Sharia FinTech. Then, it is also to contribute to the literature on the capacity of the latest technological and non-technological innovations. The research method used is descriptive research method with a qualitative approach. It is to describe and explore the phenomena in the form of engineering human innovation in the financial technology industry. It is done by taking into account the characteristics, quality, and interrelationships between activities It has several aspects; they are: conducting the observation, having an interview session, creating the documentation, and the last one is doing the Literature review. The result of this study is to increase the knowledge, skills and confidence of the community in managing personal finances to be better and to provide access to be having convenient and accountable financial services. Afterwards, this study linits on explaining and providing an understanding of the technical, procedure and benefits of Sharia Fintech for all people in need. Thence, the limitation of the research only discusses the role of Islamic Fintech in increasing the public financial inclusion and literacy. As for the the next researchers, they can be even wider by adding the collaboration of fintech and the banking world. The novelty of this research is the use of the android application as a digital platform in financial inclusion and literacy.
DETEKSI KARAKTER HURUF ARAB DENGAN MENGGUNAKAN CONVOLUTIONAL NEURAL NETWORK Ibnu Akil; Indra Chaidir
INTI Nusa Mandiri Vol. 15 No. 2 (2021): INTI Periode Februari 2021
Publisher : Lembaga Penelitian dan Pengabdian Pada Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/inti.v15i2.2179

Abstract

Dalam dunia yang serba digital bukan berarti tidak ada lagi tulisan tangan. Contohnya seperti membaca cek di bank masih harus menerima input berupa tulisan tangan. Masalahnya banyak aplikasi OCR belum bisa memfasilitasi semua bahasa salah satunya adalah bahasa arab. Karenanya diperlukan aplikasi yang dapat mengidentifikasi huruf hijaiyah tulisan tangan bahasa arab. Tujuan dari penelitian ini adalah mengembangkan aplikasi artificial intelligent untuk mendeteksi karakter huruf arab dengan metode Convolutional Neural Network. Hasil penelitian ini dapat dimanfaatkan sebagai dasar pengembangan lebih lanjut aplikasi OCR dengan banyak bahasa
PREDIKSI HARGA SAHAM TWITTER DENGAN LONG SHORT-TERM MEMORY RECURRENT NEURAL NETWORK Ibnu Akil; Indra Chaidir
INTI Nusa Mandiri Vol. 17 No. 1 (2022): INTI Periode Agustus 2022
Publisher : Lembaga Penelitian dan Pengabdian Pada Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/inti.v17i1.3277

Abstract

Abstract— Today the trading business has become a trend to get money easily without having to work hard as long as you have capital. To get maximum results and avoid losses, it is necessary to have expertise in predicting the ups and downs of the stock market value. The purpose of this research is to utilize machine learning technology to predict the fluctuation of stock value by using the Long Short-Term Memory RNN model. From the results of this study, it was found that LSTM+RNN is suitable for use in single-step models. Keywords: stock price, machine learning, recurrent neural network, lstm Abstrak—Dewasa ini bisnis trading menjadi suatu trend untuk mendapatkan uang dengan mudah tanpa harus bekerja keras asalkan memiliki modal. Untuk mendapatkah hasil yang maksimal dan menghindari kerugian maka diperlukan keahlian di dalam memprediksi naik turunya nilai bursa saham. Tujuan dari penelitian ini adalah memanfaatkan teknologi machine learning untuk memprediksi naik turunya nilai saham dengan menggunakan model Long Short-Term Memory RNN. Dari hasil penelitian ini didapatkan bahwa LSTM+RNN cocok untuk digunakan pada model single-step. Kata kunci: harga saham, machine learning, recurrent neural network, lstm
Co-Authors Aan `Rahman Abdul Hamid Ade Christian Adi Supriyatna Ali Haidir Andry Maulana Badurrachman Abdurrachman Cahya, Titus Dwi Dedi Triyanto Deny Kurniawan Dimas Wahyu Aditya Fauzi Ahmad Muda Felix Wuryo Handono Fernando B Siahaan Ginabila Hafis Nurdin Handi Hidayatullah Hariyanto Hary Sugiarto Ibnu Akil Indria Widyastuti Karlena Indriani Lise Pujiastuti Lukman Hakim Martias Martias Mochamad Wahyudi Mohammad Badrul Muhammad Iqbal Ningrum, Rina Kusuma Ninuk Riesmiyantiningtias Octa Pratama Putra Prasetyo, Romadhan Edy Riyandi Al Rino Rizky Amalia Sidauruk, Juniato Suhartono Suhartono Sumanto Sumanto Sumarna Sumarna Sumarna Syamsul Bahri Toni Wirawan Tri Budi Haryanto Wirawan, Toni Yosan Erwanto