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All Journal JTIULM (Jurnal Teknologi Informasi Universitas Lambung Mangkurat) Journal of Engineering, Electrical and Informatics (JEEI)
Fathul Hafidh
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Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering

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PENERAPAN METODE ITERATIVE DICHOTOMIZER (ID3) UNTUK DIAGNOSA HAMA TANAMAN ANGGREK Fathul Hafidh; Muhammad Edya Rosadi; Rahmadi Agus
Jurnal Teknologi Informasi Universitas Lambung Mangkurat (JTIULM) Vol. 2 No. 1 (2017)
Publisher : Fakultas Teknik Universitas Lambung Mangkurat

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (241.795 KB) | DOI: 10.20527/jtiulm.v2i1.16

Abstract

Orchid plants is one component of the aesthetic aspect and become a part of human life. Diagnostics system for classification orchid pests were attack plants still using the manual model, it’s result difficulty identifying pests orchid that has a variety of symptoms are almost the same. As to distinguish the symptoms of damage to the leaves, roots and flowers caused by pests. ID3 able to generate a decision tree of large data sets. This decision tree can be used as a reference for the diagnosis of pests on orchids. In this research, pest classification of orchids do with ID3 method with the highest level of performance accuracy is 78.06%.
PENGENALAN REAL TIME ABJAD BAHASA ISYARAT INDONESIA MENGGUNAKAN SEGMENTASI YCBCR M. Dedy Rosyadi; Fathul Hafidh; Mirza Yogy Kurniawan
Jurnal Teknologi Informasi Universitas Lambung Mangkurat (JTIULM) Vol. 2 No. 2 (2017)
Publisher : Fakultas Teknik Universitas Lambung Mangkurat

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (574.591 KB) | DOI: 10.20527/jtiulm.v2i2.18

Abstract

Bahasa isyarat tangan merupakan bentuk komunikasi utama bagi penderita tuna rungu, sedangkan untuk orang awam mempelajarinya akan memerlukan banyak biaya dan menghabiskan banyak waktu. Penelitian ini melakukan pengenalan isyarat tangan dari citra peragaan isyarat tangan menggunakan segmentasi YCbCr yang mampu memisahkan objek tangan dengan latarnya dengan cepat sehingga cocok untuk diterapkan pada pengenalan real time, kemudian untuk klasifikasi digunakan Support Vector Machine (SVM) yang sudah dikenal sebagai classifier yang mampu menghasilkan klasifikasi yang baik pada kasus citra.Tahapan penelitian ini diawali dengan pengolahan awal pada data citra, kemudian dilakukan segmentasi dengan YCbCr, pemotongan citra otomatis, dilanjutkan dengan proses klasifikasi dengan SVM. Pada akhirnya metode segmentasi yang dipilih adalah YCbCr yang mampu dengan cepat memisahkan antara objek dan latar sehingga cocok untuk proses klasifikasi real time.
APLIKASI PENJADWALAN PROGRAM PRAKTIKUM FAKULTAS TEKNOLOGI INFORMASI UNIVERSITAS ISLAM KALIMANTAN (UNISKA) MUHAMMAD ARSYAD AL BANJARI BANJARMASIN Fathul Hafidh; Muhammad Dedy Rosyadi
Jurnal Teknologi Informasi Universitas Lambung Mangkurat (JTIULM) Vol. 3 No. 2 (2018)
Publisher : Fakultas Teknik Universitas Lambung Mangkurat

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (332.818 KB) | DOI: 10.20527/jtiulm.v3i2.27

Abstract

Tujuan penelitian ini adalah memudahkan pelaksanaan program praktikum yang dilaksanakan di Fakultas Teknologi Informasi (FTI) Universitas Islam Kalimantan (UNISKA) Muhammad Arsyad Al Banjari Banjarmasin, yang mana adaya program praktikum dikarenakan FTI ingin meningkatkan kemampuan mahasiswa dalam keilmuan pemrograman yang berorientasi produk. Keinginan untuk memberikan kompetensi ini tidak dapat dilakukan melalui tatap muka perkuliahan seperti biasa dikarenakan semisal mata kuliah Pemrograman Web I hanya diajarkan dalam 16 (enam belas) pertemuan dan tidak berorientasi pada produk. Maka dari itu diperlukan praktikum untuk peningkatan skill pemrograman diluar dari jadwal perkuliahan yang telah ada. Target yang ingin dicapai adalah membuat aplikasi yang membantu dalam pendaftaran mahasiswa yang akan mengikuti kelas praktikum, membantu dosen dalam memanajemen jadwal praktikum, mencetak daftar hadir kelas praktikum dan membantu agar dosen dan mahasiswa dapat membuat dan memilih jadwal yang tidak bertabrakan dengan jadwal perkuliahan.
Enhancing Special Needs Identification for Children: A Comparative Study on Classification Methods Using ID3 Algorithm and Alternative Approaches Fathul Hafidh
Journal of Engineering, Electrical and Informatics Vol 3 No 2 (2023): Juni : Journal of Engineering, Electrical and Informatics
Publisher : Pusat Riset dan Inovasi Nasional

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55606/jeei.v3i2.1468

Abstract

This research aims to compare the performance of classification methods in identifying special needs in children. The dataset used consists of identifications of various types of special needs, such as ADHD, autism, mild cerebral palsy, mild intellectual disability, moderate intellectual disability, and hearing impairment. The methods compared include ID3 (previous study), Naive Bayes, Random Forest, k-NN, and Gradient Boosting. The comparison results show that ID3 achieves an accuracy rate of 91.81%. The new alternative methods show better performance, with Naive Bayes achieving an accuracy of 95.28%, Random Forest 95.14%, k-NN 95.28%, and Gradient Boosting 83.47%. Although Random Forest does not outperform Naive Bayes and k-NN, it has the advantage of forming decision trees that align with symptom attributes and predict disability labels. However, in the implementation of the Gradient Boosting algorithm, there is a low model probability, especially in identifying ADHD. The conclusion of this research provides insights for researchers in selecting appropriate classification methods for identifying special needs in children, considering accuracy, efficiency, and handling imbalanced data.
Enhancing Special Needs Identification for Children: A Comparative Study on Classification Methods Using ID3 Algorithm and Alternative Approaches Fathul Hafidh
Journal of Engineering, Electrical and Informatics Vol. 3 No. 2 (2023): Juni : Journal of Engineering, Electrical and Informatics
Publisher : Pusat Riset dan Inovasi Nasional

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55606/jeei.v3i2.1468

Abstract

This research aims to compare the performance of classification methods in identifying special needs in children. The dataset used consists of identifications of various types of special needs, such as ADHD, autism, mild cerebral palsy, mild intellectual disability, moderate intellectual disability, and hearing impairment. The methods compared include ID3 (previous study), Naive Bayes, Random Forest, k-NN, and Gradient Boosting. The comparison results show that ID3 achieves an accuracy rate of 91.81%. The new alternative methods show better performance, with Naive Bayes achieving an accuracy of 95.28%, Random Forest 95.14%, k-NN 95.28%, and Gradient Boosting 83.47%. Although Random Forest does not outperform Naive Bayes and k-NN, it has the advantage of forming decision trees that align with symptom attributes and predict disability labels. However, in the implementation of the Gradient Boosting algorithm, there is a low model probability, especially in identifying ADHD. The conclusion of this research provides insights for researchers in selecting appropriate classification methods for identifying special needs in children, considering accuracy, efficiency, and handling imbalanced data.
Co-Authors M. Dedy Rosyadi Mirza Yogy Kurniawan Muhammad Dedy Rosyadi Muhammad Edya Rosadi Rahmadi Agus