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All Journal International Journal of Electrical and Computer Engineering Sistemasi: Jurnal Sistem Informasi JURNAL ILMIAH MATEMATIKA DAN TERAPAN JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Vygotsky: Jurnal Pendidikan Matematika dan Matematika Jurnal Pengabdian Masyarakat Bhinneka Transcendent Journal of Mathematics and Applications Jurnal Pengabdian Farmasi dan Sains
Abdul Mahatir Najar
Tadulako University
Agriculture, Biological Sciences & Forestry Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Economics, Econometrics & Finance Education Electrical & Electronics Engineering Environmental Science Immunology & microbiology Mathematics Medicine & Pharmacology Physics Public Health

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Machine Learning untuk Identifikasi Jenis Kanker Darah (Leukemia) Abdul Mahatir Najar; I Wayan Sudarsana; M Ulul Albab; Sultan Andhika
Vygotsky : Jurnal Pendidikan Matematika dan Matematika Vol 4, No 1 (2022): Vygotsky: Jurnal Pendidikan Matematika dan Matematika
Publisher : Universitas Islam Lamongan

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (380.895 KB) | DOI: 10.30736/voj.v4i1.493

Abstract

Metode yang cepat dan tepat untuk membedakan jenis kanker darah sangat penting agar pasien kanker mendapatkan perlakuan yang sesauai. Pada penelitian ini identifikasi jenis kanker darah dilakukan dengan memanfaatkan kecerdasan buatan khususnya machine learning. Proses identifikasi kanker leukemia menggunakan machine learning dimulai dengan melakukan ektraksi ciri. Proses ektraksi ciri dilakukan dengan memanfaatkan metode Rantai Markov. Dari proses ini akan membentuk matriks yang kemudian dijadikan data training dan testing pada beberapa algoritma machine learning. Berdasarkan hasil training dan testing diperoleh hasil bahwa akurasi algoritma Decision Tree Classification memberikan hasil terbaik yaitu 83%, disusul dengan metode KNN dan sebesar 50%, sedangkan metode SVM hanya mencapai akurasi 37.5%.
Pelatihan dan Pendampingan Pemrograman Python Dalam Meningkatkan Kompetensi Siswa SMKN 5 Palu Resnawati, Resnawati; Fadjryani; Abdul Mahatir Najar; Juni Wijayanti Puspita; Aan Bin Mardi; Maulidyani Abu
JURNAL PENGABDIAN FARMASI DAN SAINS Vol. 2 No. 2 (2024): April 2024
Publisher : Jurusan Farmasi FMIPA Universitas Tadulako

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22487/jpsf.2024.v2.i2.16879

Abstract

Python programming is a multipurpose interpretive programming language with a design philosophy that focuses on code readability. Python is a language that combines capabilities with very clear code syntax and is equipped with the functionality of a large and comprehensive standard library. Python supports multiple programming paradigms. One of the features available in Python is that it is a dynamic programming language equipped with automatic memory management. This Community Service Activity is carried out by providing training material starting from a basic introduction to the Python programming language, basic rules for writing Python syntax, an introduction to variables and data types in Python, operators in Python, and learning structures and functions in Python. The mentoring process is also carried out to provide direct experience to students. This activity went well and received appreciation from the school, and the students hope that this activity can be continued so that students are able to apply Python programming well.
Prediksi Tingkat Inflasi Di Indonesia Menggunakan Metode Average Based Fuzzy Time Series Nurul Ikrima; Agus Indra Jaya; Abdul Mahatir Najar; Hajar
JURNAL ILMIAH MATEMATIKA DAN TERAPAN Vol. 20 No. 2 (2023)
Publisher : Program Studi Matematika, Universitas Tadulako

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22487/2540766X.2023.v20.i2.16582

Abstract

Inflation is a very important indicator in maintaining the stability of the country's economy, so it is necessary to predict the inflation rate to determine the movement of the inflation rate in the future. To predict the inflation rate in Indonesia in 2023, this research uses the Average Based Fuzzy Time Series (FTS) method based on Python programming. This method uses the principle of fuzzy set as the basis of its calculation. The data used in this study are monthly data on inflation rates in Indonesia for the period January 2013 - December 2022 obtained from the official website of Bank Indonesia (BI). The results showed that the prediction of the inflation rate in Indonesia in 2023 was in the range of 5.23% - 5.51% with the highest inflation value occurring in April and the lowest inflation value occurring in January. The accuracy level of the Average Based FTS method is calculated using the Mean Absolute Percentage Error (MAPE) of 0.820849835% which indicates that the method can be used to predict the inflation rate. Keywords : Average Based, FTS, Inflation, Prediction, Python
Digitalisasi Sistem Administrasi Sebagai Upaya Peningkatan Efektivitas Pelayanan di SMPN 2 Tanantovea Najar, Abdul Mahatir; Resnawati, Resnawati; Abu, Maulidyani; Andri, Andri; Gamayanti, Nurul Fiskia
Jurnal Pengabdian Masyarakat Bhinneka Vol. 2 No. 4 (2024): Bulan Juli
Publisher : Bhinneka Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58266/jpmb.v2i4.84

Abstract

Perkembangan teknologi informasi berdampak positif pada berbagai kehidupan masyarakat. Berbagai aktivitas yang dilakukan secara konvensional kemudian berubah menjadi sebuah aktivitas berbasis teknologi yang menjadikan penggunanya dimudahkan dalam mengakses berbagai informasi yang diberikan. Salah satu aktivitas penting yang sangat terdampak adalah administrasi perkantoran. Aktivitas administrasi perkantoran melibatkan berbagai pihak agar dokumen yang dibuat bisa terverifikasi dan terdokumentasi dengan baik. Administrasi perkantoran konvensional memiliki banyak kekurangan. Salah satu yang mencolok adalah waktu pengerjaan yang tidak efektif karena membutuhkan verifikasi secara tatap muka dan disusun setiap kali dibutuhkan. Digitalisasi administrasi merupakan salah satu solusi nyata yang dapat membantu efektivitas pelayanan di berbagai tempat khususnya di sekolah-sekolah. Ada berbagai macam aktivitas terkait kebutuhan para Guru, siswa, alumni, serta orang tua yang harus difasilitasi oleh pihak administrasi sekolah. Kegiatan pengabdian kepada masyarakat ini akan dilaksanakan dalam lima tahapan yakni pengumpulan kebutuhan, desain sistem, pengembangan prototype sistem, menguji dan mengevaluasi sistem serta mengimplementasikan sistem. selanjutnya dilakukan pelatihan kepada staf sekolah agar bisa langsung dapat digunakan oleh pihak sekolah. Kegiatan ini nantinya akan menghasilkan sebuah administrasi sekolah digital yang dibuat dalam sebuah aplikasi berbasis Django 3.2.
Klasifikasi Rumah Tangga Miskin Desa Siney Kecamatan Tinombo Selatan Menggunakan Metode Ordinal Class Classifier (OCC) Alfiani; Sahari, Agusman; Najar, Abdul Mahatir
JURNAL ILMIAH MATEMATIKA DAN TERAPAN Vol. 20 No. 1 (2023)
Publisher : Program Studi Matematika, Universitas Tadulako

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22487/2540766X.2023.v20.i1.16362

Abstract

Badan Pusat Statistik (BPS) mengkategorikan status rumah tangga miskin menjadi tiga yaitu Rumah Tangga Sangat Miskin (RTSM), Rumah Tangga Miskin (RTM), dan Rumah Tangga Hampir Miskin (RTHM). Mengklasifikasikan golongan rumah tangga miskin desa Siney menggunakan metode Ordinal Class Classifier (OCC). OCC merupakan meta classifier yang dapat menghasilkan suatu kelas prediksi dan dapat mengasumsikan nilai kelas nominal. Penelitian ini menggunakan data yang tersedia di kantor desa Siney sebanyak 300 data dengan 14 indikator kemiskinan. Tujuannya dapat mengklasifikasikan dan memperoleh akurasi yang optimal dari pembagian data testing dan data training dalam klasifikasi RTSM, RTM, dan RTHM berdasarkan metode OCC. Berdasarkan hasil analisis baik program maupun manual menunjukkan bahwa OCC dapat melakukan klasifikasi status rumah tangga miskin dengan baik. Hal ini dapat dilihat dari hasil Pengujian tertinggi precission sebesar 98,10%, recall 87,50% dan akurasi 96,6% OCC.Kata Kunci : Kemiskinan, Klasifikasi, Ordinal Class Classifier
Application of The C4.5 Algorithm to Get Customer Satisfaction Levels (Case Study : Toko Craft Palu, Jl. Setia Budi) Ningrum, Desy Riani Sukma; Resnawati; Najar, Abdul Mahatir; Puspita, Juni Wijayanti
JURNAL ILMIAH MATEMATIKA DAN TERAPAN Vol. 21 No. 1 (2024)
Publisher : Program Studi Matematika, Universitas Tadulako

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22487/2540766X.2024.v21.i1.16958

Abstract

Customer satisfaction refers to the response expressed by customers as a result of their evaluation of the perceived difference between their initial expectations before purchase and the performance of the service after purchase. Several specific factors impact the purchasing process and the performance of the product service, such as uncertainty in store operating hours and limited availability of inventory. These related issues have an impact on customer satisfaction, especially at Craft Palu store. The aim of this research is to determine the level of customer satisfaction and accuracy level using the decision tree method, specifically the C4.5 Algorithm. In this study, the measured variables of customer satisfaction at Craft Palu store are Tangibles, Reliability, Responsiveness, Assurance, and Empathy. Based on the results of this research, it is found that Reliability is the most influential variable with an index value is 80,6% of respondents satisfied with the 5th statement, and accuracy test results using the C4.5 Algorithm in python software show an improvement with a decent final accuracy is 90%. Therefore, the C4.5 Algortihm is suitable for measuring customer satisfaction.
Implementasi Pengolahan Citra dan Machine Learning Untuk Klasifikiasi Jenis Penyakit Pada Daun Padi Najar, Abdul Mahatir; Abu, Maulidyani; Resnawati, Resnawati; Syahrullah, Syahrullah
Transcendent Journal of Mathematics and Applications Vol 3, No 1 (2024)
Publisher : Syiah Kuala University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24815/tjoma.v3i1.38642

Abstract

Identification of diseases from images of plants is one of the interesting research areas in the agriculture field, for which machine learning concepts from the computer science field can be applied. This article presents a prototype system for the detection and classification of rice diseases based on images of infected rice plants. This prototype system was developed after detailed experimental analysis of various techniques used in image processing operations. We consider three rice plant diseases: Bacterial Leaf Blight, Blast, and Tungro. We used the Otsu method to remove the background. To enable accurate extraction of features, we combined Gabor and Sobel techniques. In the classification process, we used five machine learning techniques: Random Forest (RF), Support Vector Machine (SVM), Nave Bayes (NB), and Quadratic Discriminant Analysis (QDA). We empirically evaluated these methods, achieving 77%, 50%, 60%, and 37% accuracy, respectively.
KLASIFIKASI JENIS KAYU BERDASARKAN CITRA SERAT KAYU MENGGUNAKAN CONVOLUTIONAL NEURAL NETWORK Dwimanhendra, Muhammad Rifaldi; Syahrullah, Syahrullah; Joefrie, Yuri Yudhaswana; Angreni, Dwi Shinta; Azhar, Ryfial; Nugraha, Deny Wiria; rezandy Lapatta, Nouval; Najar, Abdul Mahatir
JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Vol 10, No 1 (2025)
Publisher : STKIP PGRI Tulungagung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29100/jipi.v10i1.5726

Abstract

Kayu merupakan sumber daya alam yang sangat penting bagi industri mebel atau furnitur. Pemilihan jenis kayu yang tepat sangat krusial dalam industri mebel untuk menentukan kualitas hasil produksi. Pemilihan kayu secara manual memiliki risiko kesalahan yang dapat berdampak negatif pada kualitas akhir produk mebel. Oleh karena itu, diperlukan penerapan teknologi untuk meminimalkan kesalahan pemilihan jenis kayu dan meningkatkan efisiensi proses produksi. Penelitian ini bertujuan membangun model klasifikasi jenis kayu (nantu, palapi, dan uru) berbasis Convolutional Neural Network (CNN) menggunakan citra serat kayu. Dataset terdiri dari 1.584 citra yang dibagi menjadi 80% data pelatihan dan 20% data pengujian. Arsitektur model CNN terdiri dari 4 lapisan konvolusi, 4 lapisan pooling, dan 2 lapisan fully-connected. Hasil pelatihan mencapai akurasi 97,06%, sedangkan hasil pengujian dan evaluasi menggunakan matriks konfusi mencapai akurasi 95,56%. Penelitian ini membuktikan bahwa CNN dapat digunakan secara efektif untuk klasifikasi jenis kayu dengan tingkat akurasi yang tinggi, sehingga dapat membantu meningkatkan efisiensi proses produksi mebel.
Low-dose computed tomography image denoising using graph wavelet transform with optimal base Setiawan, Iwan; Hidayat, Rachmat; Najar, Abdul Mahatir; Jaya, Agus Indra; Rosiyadi, Didi
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 2: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i2.pp1696-1708

Abstract

Noise in electronic components of computed tomography (CT) detectors behaves like a virus that infects visual quality of CT scans and might distort clinical diagnosis. Modern CT detector technology incorporates high-quality electronic components in conjunction with signal and image processing to ensure optimal image quality while retaining benign doses of x-rays. In this study, a new strategy in signal and image processing directions is proposed by finding the most optimal wavelet base for denoising low-dose CT scan data. The process begins by selecting the appropriate wavelet bases for CT image denoising, followed by a wavelet decomposition, thresholding, and reconstruction. Other methods, such as graph wavelet and learning-based, are used to assess the consistency of the outcomes. The wavelet base of biorthogonal 5.5 achieves the highest optimum performance for CT image denoising. Meanwhile, the Daubechies wavelet base is inconsistent and performs poorly compared to the optimum base. This research highlights the importance of wavelet properties such as orthogonality, regularity, and the number of vanishing moments in selecting an appropriate wavelet basis for noise reduction in CT images.
Identification of Maleo (Macrocephalon Maleo) and Gosong Kaki Merah (Megapodius Reindwardt) DNA Similarity Level Using Needleman-Wunsch Algorithm Rusmi, Rusmi; Ratianingsih, Rina; Najar, Abdul Mahatir
Transcendent Journal of Mathematics and Applications Vol 2, No 2 (2023)
Publisher : Syiah Kuala University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24815/tjoma.v2i2.34313

Abstract

Sequence Alignment is used to find out the similarity of the sequence of two DNA. One of the alignment algorithms is the Needleman-Wunsch algorithm which is a global alignment algorithm that uses the entire length of the DNA sequence. In this research, the algorithm is applied to a website-based application system to determine the level of similarity of the DNA sequence of Maleo (Macrocephalon Maleo) which is an endemic animal to Sulawesi, with a comparison of Gosong Kaki Merah (Megapodius Reinwardt) which has a wide global distribution. The results of the alignment of Maleo (Macrocephalon Maleo) and Kaki Merah (Megapodius Reinwardt) DNA sequences on a website-based application system have an average similarity level of 83.39% and an average gap value of 8.42%.
Co-Authors Aan Bin Mardi Agus Indra Jaya Agusman Sahari Alfiani Andri Andri Deny Wiria Nugraha Dwi Shinta Angreni Dwimanhendra, Muhammad Rifaldi Fadjryani, Fadjryani Gamayanti, Nurul Fiskia Hajar Hajra Rasmita Ngemba Hayyan Ahmad Ulul Albab I Wayan Sudarsana Iwan Setiawan Juni Wijayanti Puspita Maulidyani Abu Maulidyani Abu Ningrum, Desy Riani Sukma Nouval Trezandy Lapatta Nurul Ikrima RACHMAT HIDAYAT Resnawati Rina Ratianingsih Rosiyadi, Didi Rusmi, Rusmi Ryfial Azhar, Ryfial Sultan Andhika Syahrullah Syahrullah Syaiful Hendra Yuri Yudhaswana Joefrie