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All Journal Jurnal CoreIT Jurnal Nasional Komputasi dan Teknologi Informasi Systemic: Information System and Informatics Journal Jurnal Pengabdian Al-Ikhlas Technologia: Jurnal Ilmiah
Haldi Budiman
Universitas Islam Kalimantan Muhammad Arsyad Al Banjari
Aerospace Engineering Automotive Engineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Health Professions Neuroscience Social Sciences Transportation

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Analisis Dan Perbandingan Akurasi Model Prediksi Rentet Waktu Support Vector Machines Dengan Support Vector Machines Particle Swarm Optimization Untuk Arus Lalu Lintas Jangka Pendek Haldi Budiman
Systemic: Information System and Informatics Journal Vol. 2 No. 1 (2016): Agustus
Publisher : Program Studi Sistem Informasi Fakultas Sains dan Teknologi, UIN Sunan Ampel Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (828.097 KB) | DOI: 10.29080/systemic.v2i1.103

Abstract

Many algorithms that can be used to predict the traffic flow, there are some who are known algorithms which have a more accurate performance and some are off in the performance test the accuracy of the algorithm. For this algorithm needs to be tested to find out. The proposed method is SVM, SVM-PSO. Compared this method in neural network-based algorithm that has been in curatorial commentary for UJIA rentettime prediction data. Algorithms to be tested is SVM, SVM-PSO and Neural Network, which used the data to predict short-term traffic flow. Each of these algorithms will be implemented by using RapidMiner5.1.Performance measurement is doneby calculating the average amount of error that occurs through Root Mean Square Error(RMSE). The smaller the valueof each of the stated performance parameters predicted value closer to the true value. Thus it can be seen that the algorithm is more accurate.
PELATIHAN APLIKASI MICROSOFT WORD 2013 PADA SMP H. A. JOHANSYAH. A BANJARMASIN Yusri Ikhwani; Haldi Budiman; Muhammad Rasyidan
JURNAL PENGABDIAN AL-IKHLAS UNIVERSITAS ISLAM KALIMANTAN MUHAMMAD ARSYAD AL BANJARY Vol 1, No 1 (2015): AL-IKHLAS JURNAL PENGABDIAN
Publisher : Universitas Islam kalimantan MAB

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (167.247 KB) | DOI: 10.31602/jpai.v1i1.295

Abstract

Microsoft Word is one of the software / word processing program created by Microsoft and included in bandle Microsoft Office. Microsoft Word into a word processing program that is reliable and most widely used by computer users. So with the Microsoft Word so provide enormous benefits to the world of technology is constantly evolving, in terms of word processors used for productive activities, educational and various other things that require a powerful word processing application.
ALGORITMA ONE TIME PASSWORD PADA SISTEM INFORMASI PENERIMAAN SISWA BARU ONLINE SMP H.A. JOHANSYAH.A BANJARMASIN Herry Adi Chandra; Yusup Indra Wijaya; Haldi Budiman
Technologia : Jurnal Ilmiah Vol 10, No 4 (2019): TECHNOLOGIA (OKTOBER)
Publisher : Universitas Islam Kalimantan Muhammad Arsyad Al Banjari

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (495.227 KB) | DOI: 10.31602/tji.v10i4.2425

Abstract

Abstrak Untuk sebuah sekolah swasta yang selalu ramai saat masa pendaftaran siswa baru, SMP H.A. Johansyah.A Banjarmasin tentunya memerlukan sistem yang dapat memudahkan proses pendaftaran siswa baru karena data tersebut harus dilaporkan kepada kepala sekolah serta staff administrasi lainnya. Jika selama ini mereka hanya melakukannya dengan sistem pencatatan manual kemudian direkap dengan menggunakan microsoft excel hingga memerlukan waktu yang lebih lama ketika data dibutuhkan. Untuk mempermudah proses tersebut, kami merancang sebuah aplikasi pendaftaran siswa baru pada SMP H.A. Johansyah.A Banjarmasin berbasis web dengan menggunakan metode One Time Password untuk lebih meningkatkan keamanan serta sebagai validasi saat proses pendaftaran dilakukan secara online dan dengan adanya aplikasi ini diharapkan dapat memudahkan proses pendaftaran siswa baru nantinya. Kata Kunci : Algoritma One Time Password , Sistem Informasi, Online 
Klasifikasi Ekspor Impor Produk Pertanian dengan Metode Deep Learning Oktavia, Samita; Mambang, Mambang; Prasetya, M. Riko Anshori; Nurhaeni, Nurhaeni; Naparin, Husni; Budiman, Haldi
Jurnal Nasional Komputasi dan Teknologi Informasi (JNKTI) Vol 7, No 5 (2024): Oktober 2024
Publisher : Program Studi Teknik Komputer, Fakultas Teknik. Universitas Serambi Mekkah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32672/jnkti.v7i5.8083

Abstract

Abstrak - Perubahan nilai impor dan ekspor memiliki pengaruh signifikan terhadap pertumbuhan ekonomi suatu negara, dengan inflasi berperan penting dalam mempengaruhi neraca perdagangan. Teknik pembelajaran mesin, khususnya Deep Learning yang merupakan subset dari Machine Learning, menawarkan solusi efektif untuk mengklasifikasi dan mendiagnosis pola dalam data ekspor-impor pertanian. Menggunakan Artificial Neural Network (ANN), yang terinspirasi oleh struktur otak manusia, teknik ini dapat memproses data kompleks untuk mendukung pengambilan keputusan yang tepat dalam analisis perdagangan pertanian. Penelitian ini fokus pada penerapan Deep Learning untuk mengidentifikasi pola ekspor-impor pertanian dengan akurasi tinggi, mencapai 93.7%, dan precision sebesar 89.6%, Recall sempurna sebesar 100% dan F-Measure yang tinggi pada 94.5% menunjukkan keseimbangan antara precision dan recall.Kata kunci: Artificial Neural Network, Deep Learning, Ekspor, Impor, Klasifikasi. Abstract - The changes in import and export values have a significant impact on a country's economic growth, with inflation playing a crucial role in influencing the trade balance. Machine learning techniques, particularly Deep Learning, a subset of Machine Learning, offer effective solutions for classifying and diagnosing patterns in agricultural export-import data. Using Artificial Neural Networks (ANN), inspired by the structure of the human brain, this technique can process complex data to support accurate decision-making in agricultural trade analysis. This research focuses on the application of Deep Learning to identify agricultural export-import patterns with high accuracy, achieving 93.7%, precision of 89.6%, perfect recall of 100%, and a high F-Measure of 94.5%, indicating a balance between precision and recall.Keywords: Artificial Neural Network, Classification, Deep Learning, Ekspor, Impor
Review of Original Differential Evolution Algorithm: Research Trends, Original Setting Parameters Wang, ShirLi; Budiman, Haldi; Ramadhani, Siti; FooNg, Theam; Morsidi, Farid
Jurnal CoreIT: Jurnal Hasil Penelitian Ilmu Komputer dan Teknologi Informasi Vol 10, No 2 (2024): December 2024
Publisher : Fakultas Sains dan Teknologi, Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/coreit.v10i2.29903

Abstract

Abstract: Differential Evolution (DE) has emerged as a widely embraced optimization algorithm, consistently showcasing robust performance in the IEEE Congress on Evolutionary Computation (CEC) competitions.Purpose: This study aims to pinpoint key regulatory parameters and manage the evolution of DE parameters. We conduct an exhaustive literature review spanning from 2010 to 2021 to identify and analyze evolving trends, parameter settings, and ensemble methods associated with original differential evolution.Method: Our meticulous investigation encompasses 1,210 publications, comprising 543 from ScienceDirect, 12 from IEEE Xplore, 424 from Springer, and 231 from WoS. Through an initial screening process involving title and abstract skimming to identify relevant subsets and eliminate duplicate entries, we excluded 762 articles from full-text scrutiny, resulting in 358 articles for in-depth analysis.Findings: Our findings reveal a consistent utilization of tuning parameters, self-adaptive mechanisms, and ensemble methods in the final collection. These results deepen our understanding of DE's success in CEC competitions.Value: offer valuable insights for future research and algorithm development in optimization fields.  
Validasi Gerakan Sit-Up dengan Penerapan Konsep Trigonometri dalam Simulasi Python Rahmini Rahmini; Septyan Eka Prastya; Muhammad Zulfadhilah; Muhammad Ziki Elfirman; Usman Syapotro; Haldi Budiman
Jurnal Nasional Komputasi dan Teknologi Informasi (JNKTI) Vol 8, No 5 (2025): Oktober 2025
Publisher : Program Studi Teknik Komputer, Fakultas Teknik. Universitas Serambi Mekkah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32672/jnkti.v8i5.9765

Abstract

Abstrak - Aktivitas sit-up merupakan salah satu latihan fisik yang umum digunakan untuk melatih kekuatan otot perut, namun sering dilakukan dengan teknik yang salah sehingga berpotensi menimbulkan cedera. Penelitian ini bertujuan mengembangkan sistem validasi gerakan sit-up berbasis computer vision dengan pendekatan trigonometri untuk mendeteksi kesesuaian sudut tubuh. Metode yang digunakan adalah pemanfaatan framework MediaPipe untuk ekstraksi titik sendi (keypoints), kemudian dilakukan perhitungan sudut pinggul dan lutut menggunakan aturan kosinus trigonometri. Validasi dilakukan dengan kriteria sudut pinggul 50°–100° dan lutut 60°–110°. Sistem diimplementasikan menggunakan Python dan Flask sebagai antarmuka. Hasil pengujian menunjukkan bahwa metode ini mampu mengidentifikasi gerakan sit-up dengan tingkat akurasi tinggi dan memberikan umpan balik secara real-time. Penelitian ini membuktikan bahwa kombinasi computer vision dan trigonometri dapat digunakan secara efektif dalam validasi gerakan olahraga.Kata kunci: Sit-up; Validasi Gerakan; MediaPipe; Trigonometri; Computer Vision; Abstract - Sit-up is one of the most common physical exercises for strengthening abdominal muscles, but it is often performed incorrectly, leading to a high risk of injury. This study aims to develop a sit-up movement validation system based on computer vision using trigonometric approaches to detect body angle conformity. The method applies the MediaPipe framework to extract body keypoints, followed by angle calculation of the hip and knee joints using the cosine rule of trigonometry. Validation is conducted using hip angle criteria of 50°–100° and knee angle criteria of 60°–110°. The system is implemented in Python with Flask as the user interface. Experimental results show that this method successfully identifies sit-up movements with high accuracy and provides real-time feedback. This study demonstrates that combining computer vision and t
Exploratory Data Analysis Pernikahan di Kabupaten Banjar dengan Pendekatan Machine Learning Nor Azizah; Mambang Mambang; Subhan Panji Cipta; Muhammad Zulfadhilah; Usman Syapotro; Haldi Budiman
Jurnal Nasional Komputasi dan Teknologi Informasi (JNKTI) Vol 8, No 3 (2025): Juni 2025
Publisher : Program Studi Teknik Komputer, Fakultas Teknik. Universitas Serambi Mekkah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32672/jnkti.v8i3.9063

Abstract

Abstrak - Pernikahan adalah ikatan suci antara seorang pria dan wanita yang dilakukan dengan tujuan beribadah kepada Allah SWT. Tujuan dari pernikahan adalah untuk membentuk keluarga yang sakinah (tentram), mawaddah (cinta), dan rahmah (kasih sayang). Laporan Statistik Indonesia mencatat 1,7 juta pernikahan di Indonesia pada tahun 2022, turun 2,1% dari 1,74 juta pernikahan tahun 2021. Ini adalah angka terendah dalam sepuluh tahun terakhir, didorong oleh tren pernikahan yang menurun di Indonesia sejak 2012, yang merupakan angka tertinggi dalam sepuluh tahun terakhir. Penelitian ini bertujuan untuk mengkaji dataset pernikahan di Kabupaten Banjar dengan fokus pada hubungan antara jumlah pernikahan, pendidikan, dan usia pengantin menggunakan Exploratory Data Analysis (EDA) dan machine learning. Hasil penelitian menunjukkan korelasi kuat nilai 0,99 hingga 1 antara jumlah pernikahan, usia dan pendidikan pasangan. Kelompok usia 21–30 tahun dan dengan tingkat pendidikan SLTA memiliki tingkat pernikahan tertinggi. Hasil ini menunjukkan bahwa Exploratory Data Analysis (EDA) sangat penting untuk memahami pola sosial berdasarkan analisis data statistik.Kata kunci: exploratory data analysis; machine learning; pernikahan; supervised learning; python. Abstract - Marriage is a sacred bond between a man and a woman, undertaken with the intention of worshiping Allah SWT. The purpose of marriage is to build a family that is sakinah (peaceful), mawaddah (full of love), and rahmah (compassionate). According to the Indonesian Statistics Report, there were 1.7 million marriages in Indonesia in 2022, marking a 2.1% decrease from 1.74 million marriages in 2021. This represents the lowest number of marriages in the past decade, driven by a declining marriage trend in Indonesia since 2012, which was the peak year in that period. This study aims to examine the marriage dataset in Banjar Regency, focusing on the relationship between the number of marriages, educational background, and the age of the bride and groom using Exploratory Data Analysis (EDA) and machine learning. The results show a strong correlation, with values ranging from 0.99 to 1, between the number of marriages, age, and education levels of the couples. The highest marriage rates were observed among individuals aged 21–30 with a senior high school (SLTA) education level. These findings highlight the importance of Exploratory Data Analysis (EDA) in understanding social patterns through statistical data analysis.Keywords: exploratory data analysis; machine learning; marriage; supervised learning; python.
Co-Authors Chandra, Herry Adi FooNg, Theam Mambang Morsidi, Farid Muhammad Rasyidan Muhammad Ziki Elfirman Muhammad Zulfadhilah Naparin, Husni Nor Azizah Nurhaeni Nurhaeni Oktavia, Samita Prasetya, M. Riko Anshori Rahmini Rahmini Ramadhani, Siti Septyan Eka Prastya Subhan Panji Cipta Usman Syapotro Wang, ShirLi Wijaya, Yusup Indra Yusri Ikhwani