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All Journal Jurnal TIMES Sinkron : Jurnal dan Penelitian Teknik Informatika IJIIS: International Journal of Informatics and Information Systems Brahmana : Jurnal Penerapan Kecerdasan Buatan
Tambunan, Fazli Nugraha
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Computer Science & IT Decision Sciences, Operations Research & Management Education Engineering

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Comparison of Tomato Leaf Disease Classification Accuracy Using Support Vector Machine and K-Nearest Neighbor Methods Zer, P.P.P.A.N.W. Fikrul Ilmi R.H.; Tambunan, Fazli Nugraha; Rosnelly, Rika; Wanayumini, Wanayumini
Sinkron : jurnal dan penelitian teknik informatika Vol. 7 No. 2 (2023): Research Article, Volume 7 Issue 2 April, 2023
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v8i2.12195

Abstract

Tomato Leaf Disease is one of the common things for farmers in growing tomatoes. Tomatoes are one of the popular crops that can grow in low and high areas but are susceptible to disease. For this reason, farmers take precautions by looking at the characteristics and texture of tomato leaves. However, this requires more time and money and a long process. One of the efforts that can be made is to classify tomato leaf diseases. This research aims to classify using the Support Vector Machine and K-Nearest Neighbor methods. The dataset used is tomato leaf image data with 4 classes of leaves affected by disease and 1 healthy leaf. We evaluate and analyze all models using 5-Fold, 10-Fold, and 20-Fold Cross Validation with accuracy, precision, and recall for the best accuracy. The best results of this study are accuracy in the SVM method of 0.953 or 95.3%, Precision of 0.953 or 95.3%, and Recall of 0.953 or 95.3% with 10-Fold Cross-Validation. Compared to the K-NN method, it only obtained an accuracy of 0.907 or 90.7%, a Precision of 0.908 or 90.8%, and a Recall of 0.907 or 90.7% with 10-Fold Cross-Validation.
Certainty Factor Method Analysis for Identification of Covid-19 Virus Accuracy Hayadi, B Herawan; Widawati, Enny; Bachtiar, Marsellinus; Tambunan, Fazli Nugraha
International Journal of Informatics and Information Systems Vol 6, No 1: January 2023
Publisher : International Journal of Informatics and Information Systems

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/ijiis.v6i1.156

Abstract

Corona virus or often called COVID-19 is a virus caused by SARS CoV 2, where the incident was uploaded in the world of health or we often call WHO. Even the World Health Organization (WHO) has declared that the corona virus outbreak is a Public Health Emergency of International Concern (PHEIC) or an international public health emergency. Not only has an impact on health, but this virus outbreak has also had a major impact in various sectors such as disrupting the country's economy, disrupting the education process and so on. This impact is caused by the very fast spread of the virus. Therefore, the author will analyze the level of accuracy in the covid-19 virus by using the certainty method model which aims to make it easier for local governments to monitor the spread of the COVID-19 virus and can determine future policies so that the spread is not more easily exposed to the community. this method will produce data analysis and diagnoses regarding identifying the covid-19 virus with results in the form of accuracy, namely someone is indicated as COVID-19 POSITIVE.
Penerapan Algoritma K-Means Dalam Clustering Data Seleksi Benih Kelapa Sawit Di PPKS Unit Marihat Tambunan, Fazli Nugraha
Brahmana : Jurnal Penerapan Kecerdasan Buatan Vol 5, No 2 (2024): Edisi Juni
Publisher : LPPM STIKOM Tunas Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/brahmana.v5i2.465

Abstract

Oil palm is one of the most useful gardens in daily life. In addition to producing vegetable oil, oil palm seeds can also be marketed. In this study, palm oil seed data will be clustered using the K-Means method to determine the results of good seeds. Clustering means a method of analyzing data whose purpose is to group data with the same characteristics and characteristics in an area. While K-Means is a data analysis method or Data Mining method that performs unsupervised modeling process and is one of the methods that classify data with a partition system (division of an object into several parts with a specific purpose). The K-Means method seeks to group existing data into several groups, where data in one group has similar characteristics to each other and has different characteristics from data in other groups. In other words, this method tries to minimize the variation between data in a cluster and maximize the variation with data in other clusters.
PENGENALAN POLA KEMAMPUAN PELANGGAN DALAM MEMBAYAR AIR PDAM MENGGUNAKAN ALGORITMA NAÏVE BAYES Ilmi R.H. Zer, P.P.P.A.N.W. Fikrul; Batubara, Ela Roza; Alkhairi, Putrama; Tambunan, Fazli Nugraha; Rosnelly, Rika
Jurnal TIMES Vol 10 No 2 (2021): Jurnal TIMES
Publisher : STMIK TIME

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (828.686 KB) | DOI: 10.51351/jtm.10.2.2021656

Abstract

Dengan meningkatnya jumlah MBR (Masyarakat Berpenghasilan Rendah) yang masuk setiap tahunnya dimasing-masing wilayah di Pematansgsiantar, pihak PDAM Tirta Lihou berencana mencari alternatif solusi dalam menangani permasalahan kemampuan pelanggan dalam membayar tagihan air sehingga biaya opersional tetap bisa berjalan baik dan produksi dapat memenuhi kebutuhan masyarakat. Dalam menentukan alternatif untuk menentukan kemampauan masyarakat dalam membayar tagiahan air digunakan metode datamining. Dengan menggunakan teknik datamining khususnya klasifikasi menggunakan algoritma Naive Bayes dapat dilakukan prediksi terhadap kemampauan pelanggan dalam membayar tagihan air bersih berdasarkan data yang ada. Naive bayes adalah teknik prediksi probabilistik sederhana yang berdasarkan pada teorema Bayes dengan asumsi independensi (ketidak tergantungan) yang kuat. Berdasarkan hasil dari perhitungan menggunakan algoritma Naive Bayes, diperoleh hasil klasifikasi dari 30 alternatif yang digunakan, dimana terdapat 11 kelas mampu membayar tagihan dan 19 Tidak Mampu dengan total Accuracy yang diperoleh sebesar 70%. Dari hasil yang diperoleh,diharapkan penelitian ini dapat membantu pihak PDAM Tirta Lihou dalam menentukan lokasi yang layak dilakukan penaybungan sumber air untuk pelanggan yang memiliki prosfek baik dengan kemampuan untuk membayar tagihan air, sehingga dapat meminimalisir kerugian PDAM dan dapat memenuhi kebutuhan masyarakat. Penelitian ini juga diharapkan dapat menjadi referensi bagi peneliti selanjutnya yang berkaitan dengan pengguna algoritma yang digunakan.
Co-Authors Alkhairi, Putrama B. Herawan Hayadi Bachtiar, Marsellinus Batubara, Ela Roza Enny Widawati Ilmi R.H. Zer, P.P.P.A.N.W. Fikrul Rika Rosnelly, Rika Wanayumini Zer, P.P.P.A.N.W. Fikrul Ilmi R.H.