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All Journal Teknosains: Media Informasi Sains dan Teknologi JUSIFO : Jurnal Sistem Informasi Sistemasi: Jurnal Sistem Informasi JURNAL TEKNIK INFORMATIKA DAN SISTEM INFORMASI Jurnal Informatika Global Jurnal INFOTEL Jurnal Software Engineering and Computational Intelligence Journal of Intelligent Networks and IoT Global
Muhammad Haviz Irfani
Universitas Indo Global Mandiri
Agriculture, Biological Sciences & Forestry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Physics

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Analisis Penerimaan Sistem Informasi Akademik Dengan Menggunakan UTAUT 2 (Studi Kasus: Akademi Keperawatan Pembina Palembang) Siska Anggraini; Muhammad Haviz Irfani; Sri Rahayu
JUSIFO : Jurnal Sistem Informasi Vol 6 No 1 (2020): JUSIFO (Jurnal Sistem Informasi) | June 2020
Publisher : Program Studi Sistem Informasi, Fakultas Sains dan Teknologi, Universitas Islam Negeri Raden Fatah Palembang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.19109/jusifo.v6i1.5616

Abstract

Penerimaan terhadap sistem teknologi informasi penting untuk dilakukan karena dapat menjadi indikator bahwa sistem akan diterima dan diterapkan oleh pengguna untuk mendukung proses perkuliahan di Akademi Keperawatan Pembina Palembang. Tujuan dari penelitian ini adalah untuk mengetahui bagaimana pengaruh variabel model Unified Theory of Acceptance and Use of Technology (UTAUT) 2 terhadap penerimaan sistem informasi akademik dan seberapa besar tingkat penerimaan sistem informasi akademik menggunakan model UTAUT 2. Untuk mengetahui penerimaan sistem informasi peneliti menggunakan semua variabel utama dan variabel moderasi umur, jenis kelamin, pengalaman. Data penelitian ini menggunakan 135 responden yaitu 113 sampel mahasiswa dan 22 sampel dosen melalui penyebaran kuesioner. Analisis data yang digunakan yaitumenggunakan metode Structural Equation Modeling (SEM) dengan menggunakan tool Lisrel versi 8.70. Analisis SEM memiliki tahapan yaitu: (1) konseptualisasi model (2) membentuk path diagram (3) identifikasi model (4) estimasi model (5) penilaian model fit (6) menginterpretasikan hasil. Hasil penelitian menunjukan bahwa variabel espektasi kinerja berpengaruh sebesar (16.46), espektasi usaha berpengaruh sebesar (16.54), pengaruh sosial berpengaruh sebesar (14.85), motivasi hedonis berpengaruh sebesar (6.18), nilai harga berpengaruh sebesar (16.59), kebiasaan berpengaruh sebesar (15.94) terhadap niat perilaku. Variabel kondisi fasilitas berpengaruh sebesar (2.22) terhadap perilaku menggunakan. Variabel UTAUT 2 mampu mempengaruhi penerimaan sistem sebesar 9,4%.
Pengaruh Kemampuan Numerik dan Algoritma terhadap Kemampuan Pemrograman dalam Pilihan Tema Skripsi Muhammad Haviz Irfani
Sistemasi: Jurnal Sistem Informasi Vol 10, No 1 (2021): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (2559.968 KB) | DOI: 10.32520/stmsi.v10i1.1088

Abstract

AbstrakKeberhasilan mahasiswa dalam lingkungan program studi Sistem Informasi UIN Raden Fatah Palembang menyelesaikan tugas akhir atau skripsi sangat ditentukan oleh tema skripsi yang dipilih. Mahasiswa cenderung untuk menghindari penelitian dalam konteks  pengembangan sistem (membuat aplikasi/ coding) sehingga mempengaruhi mahasiswa lainnya untuk melakukan hal yang sama setiap semesternya. Kenyataannya kemampuan membuat kode bahasa pemrograman atau melakukan penelitian analisis (tidak membuat aplikasi) keduanya berkontribusi dalam membuat keputusan menentukan tema skripsi. Penelitian ini bertujuan untuk mengetahui seberapa besar pengaruh kemampuan numerik dan logika, dan algoritma terhadap kemampuan membuat kode bahasa pemrograman untuk hasil pilihan tema skripsi mahasiswa program studi Sistem Informasi Universitas Islam Negeri Raden Fatah Palembang. Penting diteliti faktor kemampuan numerik dan logika, kemampuan analisis data, kemampuan algoritma dan pemrograman mempengaruhi kemampuan mahasiswa membuat kode bahasa pemrograman, serta secara simultan pengaruhnya terhadap hasil pilihan tema skripsi. Data hasil studi mahasiswa diolah menggunakan Lisrel 8.80, selain itu uji prasyarat analisis SEM yang digunakan dalam penelitian (berupa uji asumsi kecukupan sampel, uji klasik, dan evaluasi outlier, dan Uji fit model. Mahasiswa Sistem Informasi dalam memilih tema skripsi (membuat kode program) tidak terlalu besar dipengaruhi secara bersama-sama oleh kemampuan numerik dan logika, kemampuan analisis data, kemampuan algoritma dan pemrograman, dan juga kemampuan membuat program.Kata Kunci: algoritma, logika, numerik, statistik, structural equation modeling AbstractThe successful of students in the Information System study program of Islamic State University of Raden Fatah Palembang in completing their final project or thesis is largely determined by their thesis theme. Students tend to avoid research in the context of system development (making applications / coding) so as to influence other students to do the same thing every semester. In fact, the ability to code a programming language or conduct analytical research (not to create applications) both contributes to the decision to determine the thesis theme. This study aims to determine how is the influence numerical and logical, data analysis, programming and algorithmic abilities on the ability to code programming languages for the thesis theme choices of students of the Information Systems study program of Islamic State University of Raden Fatah Palembang. It is important to examine the factors of numerical ability, data analysis skills, logical and algorithmic abilities affecting students' ability to code programming languages, and simultaneously these effects on the results of the thesis theme choice. Students final results were processed using Lisrel 8.80, besides the prerequisite tests for SEM (Structural Equation Model) analysis used in this study was in the form of assumptions on sample adequacy test, classic test, and evaluation of outliers, and model fit test. Information Systems students in choosing a thesis theme (making program code) were not highly influenced by numerical and logical abilities, data analysis skills, algorithmic and programming abilities , and also the ability to create programs.Keywords: algorithm, logic, numeric, statistics, structural equation modeling
Pengaruh Layanan Google Terhadap Motivasi Belajar Untuk Mendukung Prestasi Belajar Siswa Muhammad Haviz Irfani; Daniel Udjulawa
JATISI (Jurnal Teknik Informatika dan Sistem Informasi) Vol 4 No 1 (2017): JATISI SEPTEMBER 2017
Publisher : Lembaga Penelitian dan Pengabdian pada Masyarakat (LPPM) STMIK Global Informatika MDP

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (464.507 KB) | DOI: 10.35957/jatisi.v4i1.90

Abstract

Information technology in education sector affected on learning process held by senior high school in Palembang city. Some 77,43% computer users (including students) who visit the google pages with the number of of 4,464,000,000 visitors everyday. Particularly students are very depend on search engine to to search of information or matter to complete a task school that relies heavily with the internet, thereby should be tested what factors affecting and could provide motivation to study and improve student learning achievements. The results of literature give you some of factors affect the motivation to study and student learning achievements that is a source of learning, intensity using, the quality of information. The methodology used namely AMOS (Analysis of Moment Structure), and path analysis, calculation the probability between endogenous, or endogenous and exogenous by the application of SEM (Structural Equation Model). The results of the study gained may be used as input for school management to make maximum use of google search engine in increased the motivation to study and student learning achievements
Perbandingan Akurasi Metode Principal Component Analysis (PCA) dan Correlation-Based Feature Selection (CFS) Pada Klasifikasi Perpanjangan Kontrak Karyawan Menggunakan Metode Naïve Bayes Dewi Sartika; Imelda Saluza; Muhammad Haviz Irfani
Jurnal Informatika Global Vol 13, No 2
Publisher : UNIVERSITAS INDO GLOBAL MANDIRI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36982/jiig.v13i2.2292

Abstract

PT. Oasis Waters International Palembang conducts regular staff performance reviews, the findings of which are utilized to make recommendations for employee contract extension. The Human Resource Department has assigned a numerical value to 25 qualities (HRD). The process of giving a label or class to a number of examples when the value of each characteristic is known as classification. The Naïve Bayes technique is a basic classification approach that makes use of probability estimates. Based on the observations, it was discovered that one of the 25 criteria was deemed the most relevant in determining the recommendation for an employee contract renewal. As a result, in this study, a comparison of the pre-processing Principal Component Analysis (PCA) approach and the Correlation-based Feature Selection (CFS) method on the categorization of employee contract extensions at PT Oasis Waters International Palembang will be performed. According to the data, the CFS approach has a positive influence on classification performance, while PCA does not. This is demonstrated by a 30% increase in accuracy when utilizing the CFS approach. Meanwhile, both strategies have a positive influence on the model's dependability. This is demonstrated by a reduction in Root Mean Square Error (RMSE) when using the CFS approach from 0.6325 to 0.1845, whereas using the PCA method results in 0.5123.Keywords : Naïve Bayes, Principal Component Analysis, Correlation-based Feature Selection, Confusion Matrix, Root Mean Square Error
Permainan INGBAS (Gunting, Batu, Kertas) Menggunakan Arsitektur Convolutional Neural Network muhammad haviz irfani
JATISI (Jurnal Teknik Informatika dan Sistem Informasi) Vol 10 No 1 (2023): JATISI (Jurnal Teknik Informatika dan Sistem Informasi)
Publisher : Lembaga Penelitian dan Pengabdian pada Masyarakat (LPPM) STMIK Global Informatika MDP

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35957/jatisi.v10i1.2891

Abstract

In this research, an image classification model was developed to distinguish hand objects display scissors, rock, and paper using one of the image classification methods in the form of a Convolutional Neural Network (CNN). There are eight steps involved in determining the legibility of an image when an image file is recognized by uploading it from the computer's internal storage. The eight stages are source data retrieval, INGBAS data categorization, data visualization, import library using the Convolutional Neural Network (CNN) algorithm, data training, data accuracy and validation, image prediction, and conclusions. Based on the testing in this study, the average value of accuracy is 90%.
Penjadwalan Mata Pelajaran Menggunakan Algoritma Particle Swarm Optimization (PSO) Pada SMPIT Mufidatul Ilmi Muhammad Muhardeny; Muhammad Haviz Irfani; Juhaini Alie
Jurnal Software Engineering and Computational Intelligence Vol 1 No 1 (2023)
Publisher : Informatics Engineering, Faculty of Computer Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36982/jseci.v1i1.3047

Abstract

Scheduling has a division of time based on a work sequence arrangement plan in the form of a list or table of activities or an activity plan with a detailed division of implementation time which is very necessary in carrying out institutional/company business processes. It is important to note the complexity of the process in scheduling appropriate subjects from various perspectives, both teachers, students and classrooms. Provision of teacher teaching schedules based on abilities in the field of subjects, suitable time each semester is very important to consider for very complex schedule arrangements, the number of classrooms that can be used in teaching activities is relatively small, and preventing teacher teaching conflicts so that the need for optimization of eye scheduling lesson to be made. Furthermore, at the stage of application development using the Waterfall method. The purpose of this research is to build a lesson scheduling application at SMPIT Mufidatul Ilmi by applying the particle swarm optimization (PSO) algorithm to compile lesson schedules. Particle Swarm Optimization is a population-based algorithm that exploits individuals in search. In PSO the population is called a swarm and individuals are called particles. Each particle moves at a speed adapted from the search area and stores it as the best position ever achieved. Design analysis includes Use Case Diagrams, Activity Diagrams, Class Diagrams, Sequence Diagrams, Entity Relationship Diagrams (ERD). The results of this study provide several primary data (service) features, especially features to provide scheduling results from processing with the PSO algorithm
Segmentasi teks pada citra tulisan tangan kalimat menggunakan metode Median Filtering dan Otsu Irfani, Muhammad Haviz; Gasim, Gasim
Teknosains Vol 18 No 1 (2024): Januari-April
Publisher : Fakultas Sains dan Teknologi Universitas Islam Negeri Alauddin Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24252/teknosains.v18i1.44307

Abstract

Tulisan tangan seseorang mampu memberikan banyak informasi baik untuk diri sendiri maupun bagi orang lain. Citra tulisan tangan dapat memberikan bermacam bentuk citra baru hasil dari segmentasi citra/ gambar. Kesulitan untuk memilih karakter atau kata atau kalimat dari citra asli untuk berbagai pola menggunakan segmentasi citra dapat digunakan untuk memberikan informasi penting dalam pengenalan pola. Tujuan penelitian pada tahap ini yaitu melakukan segmentasi baris teks pada sebuah citra tulisan tangan dalam Bahasa Indonesia berbentuk paragraf. Metode yang digunakan dalam penelitian ini yaitu metode Median Filtering dalam perbaikan citra, dan metode Otsu untuk segmentasi citra. Penelitian ini juga menggunakan pemrograman aplikasi Matlab versi R2021b untuk memproses setiap tahapan penelitian. Hasil dari penelitian ini telah berhasil mendapatkan citra baru berbentuk baris teks atau kalimat yang berjumlah 323 gambar hasil pemisahan pada kisaran Threshold sebesar 0,18 sampai 0,55. Terdapat 38,39% gambar sulit terbaca dan 61,6% gambar yang terbaca dengan baik.
Studi Komparatif Teknik Cropping Urat Daun Jeruk dengan Metode Artificial Neural Network irfani, muhammad haviz
JATISI Vol 12 No 2 (2025): JATISI (Jurnal Teknik Informatika dan Sistem Informasi)
Publisher : Universitas Multi Data Palembang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35957/jatisi.v12i2.11429

Abstract

Citrus as a major agricultural commodity in Indonesia, plays a crucial role in the industry and farmers' income. Identification of citrus seedling types is a major challenge, due to the lack of knowledge and experience of farmers, causing potential financial and time losses. This study compares the Artificial Neural Network Backpropagation (JST-PB) method and Gray Level Co-occurrence Matrix (GLCM) features in orange seedling type identification through leaf vein images. Data was collected using a macro camera with Samsung ISOCELL GM2 sensor, with various cropping sizes on a total dataset of 1250 training images and 625 test images. The JST-BP method and GLCM features provided an accuracy rate of 91.2% at a cropping size of 200x200 piksels, 87.2% at a cropping size of 250x250 piksels, 90.4% at a cropping size of 300x300 piksels, 95.2% at a cropping size of 350x350 piksels, and the highest accuracy rate at a cropping size of 400x400 piksels, reaching 98.4%. The results of this study make an important contribution to the understanding of the identification of citrus seedling types through leaf vein images, highlighting the comparison between the JST-PB method and GLCM features at various image cropping sizes.
Klasifikasi Data Kelulusan Siswa SMK Muhammadiyah 1 Palembang Menggunakan Metode Naive Bayes Ramadhani, Kiki; Suhandi, Nazori; Irfani, Muhammad Haviz
Journal Of Intelligent Networks and IoT Global Vol 3 No 1 (2025)
Publisher : Universitas Indo Global Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36982/jinig.v3i1.5903

Abstract

Perkembangan saat ini semakin pesat seiring dengan berkembangnya teknologi informasi, untuk membantu karyawandalam menyelesaikan tugasnya dan menjamin efisiensi waktu. Didalam dunia pendidikan, penerapan data miningmemberikan peluang besar untuk membantu sekolah dan perguruan tinggi, baik negeri maupun swasta dalam memperolehwawasan yang berguna. Salah satu klasifikasi yang cocok digunakan dalam menyeleksi informasi alumni lebih lanjutadalah metode Naive Bayes karena memiliki tingkat kecepatan dan akurasi yang lebih tinggi sehingga mampu menangkapdata lebih banyak dibandingkan metode lainnya. Penelitian ini bertujuan untuk menerapkan metode Naïve Bayes dalamklasifikasi data lulusan siswa SMK Muhammadiyah 1 Palembang. Dataset diperoleh dari Wakil Kepala Sekolah SMKMuhammadiyah 1 Palembang 562 data. Hasil penelitian menunjukkan bahwa data training sebanyak 393 data denganalgoritman Naïve Bayes berhasil memprediksi besarnya kelulusan mahasiswa dengan presentase keakuratan sebesar85,80%. Data mining dan naïve bayes mampu menampilkan informasi prediksi kelulusan siswa dengan menggunakandata siswa yang telah lulus sebagai data training dan data testing. Sebanyak 169 data testing yang dihasilkan penelitianini bahwa siswa yang lulus sebanyak 110 siswa atau sekitar 65% dari jumlah data testing sebesar 85,80%. Berdasarkanhasil ini, metode naïve bayes direkomendasikan untuk klasifikasi data dalam memprediksi kelulusan siswa.
Improving the Accuracy of Concrete Mix Type Recognition with ANN and GLCM Features Based on Image Resolution Gasim, Gasim; Heriansyah, Rudi; Puspasari, Shinta; Irfani, Muhammad Haviz; Purnamasari, Evi; Permatasari, Indah; Samsuryadi, Samsuryadi
JURNAL INFOTEL Vol 17 No 1 (2025): February 2025
Publisher : LPPM INSTITUT TEKNOLOGI TELKOM PURWOKERTO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20895/infotel.v17i1.1201

Abstract

Concrete is an essential construction material that is often used due to its strength and durability, but its mix type identification often relies on conventional methods that are less efficient and accurate. This research aims to evaluate the effect of image resolution on the accuracy of concrete mix type recognition using Artificial Neural Network (ANN) and Gray-Level Co-Occurrence Matrix (GLCM) features. The method used involves analysing concrete images at various resolutions: 200 x 200, 300 x 300, 400 x 400, 500 x 500, 600 x 600, and 700 x 700 pixels. The experimental results show that higher image resolutions tend to improve recognition accuracy. all types of image sizes using 1,250 training data and 250 test data. Image sizes of 200 x 200 and 300 x 300 pixels give low accuracy of 42% and 45% respectively, while sizes of 400 x 400 and 500 x 500 pixels show an increase in accuracy to 60.5% and 62.5%. The higher resolutions of 600 x 600 and 700 x 700 pixels produced the highest accuracy of 68% and 70%, respectively. These results indicate that larger image resolutions are able to capture more details and characteristics required for more accurate concrete mix type recognition. This research has implications for improving efficiency and consistency in concrete inspection in the construction industry through the use of AI-based image recognition methods.
Co-Authors Daniel Udjulawa DEWI SARTIKA gasim, Gasim Imelda Saluza, Imelda Indah Permatasari Juhaini Alie Muhammad Muhardeny Nazori Suhandi Purnamasari, Evi Ramadhani, Kiki Rudi Heriansyah, Rudi Samsuryadi Samsuryadi Sartika, Dewi Shinta Puspasari Siska Anggraini Sri Rahayu