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Muslimin B, Muslimin
Politeknik Pertanian Negeri Samarinda
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Perbandingan Metode K-Nearest Neighbors dan Naïve Bayes Classifier Pada Klasifikasi Status Gizi Balita di Puskesmas Muara Jawa Kota Samarinda Moch. Rizky Yuliansyah; Muslimin B; Annafi Franz
Adopsi Teknologi dan Sistem Informasi (ATASI) Vol. 1 No. 1 (2022): Adopsi Teknologi dan Sistem Informasi (ATASI)
Publisher : Mulawarman University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30872/atasi.v1i1.25

Abstract

Klasifikasi adalah salah satu pembelajaran yang paling umum di dalam data mining. Klasifikasi dapat didefinisikan sebagai bentuk dari analisis data yang digunakan untuk mengekstrak model yang akan digunakan untuk memprediksi label kelas. Status Gizi adalah ukuran keberhasilan dalam pemenuhan nutrisi untuk anak yang diindikasikan oleh berat badan dan tinggi badan anak. Status gizi juga dapat didefinisikan sebagai status kesehatan yang dihasilkan oleh keseimbangan antara kebutuhan dan masukan nutrisi. Metode K-Nearest Neighbors (KNN) merupakan sebuah metode untuk melakukan klasifikasi berdasarkan kedekatan lokasi (jarak) suatu data dengan data yang lain. Prinsip kerja K-Nearest Neighbors (KNN) adalah mencari jarak terdekat antara data yang dievaluasi dengan (K) tetangga terdekatnya dalam data pelatihan. Metode Naïve Bayes Classifier (NBC) merupakan sebuah metode klasifikasi yang memanfaatkan teori probabilitas untuk memprediksi probabilitas di masa depan berdasarkan pengalaman di masa sebelumnya. Adapun tujuan dari penelitian ini adalah untuk melakukan perbandingan hasil Klasifikasi Status Gizi Balita dengan menggunakan metode K-Nearest Neighbors dan Naïve Bayes Classifier. Dari perbandingan performa antara metode K-Nearest Neighbors dan Naïve Bayes Classifier menggunakan f1 score sebagai patokan utama performa klasifikasi. Didapatkan hasil bahwa metode K-Nearest Neighbors unggul pada f1 score dengan selisih cukup besar yakni 13,42 %. Sehingga, dapat disimpulkan bahwa pada masalah klasifikasi status gizi balita metode K-Nearest Neighbors mengungguli Naïve Bayes Classifier.
Decision Support System for Selection of Productive Land in Corn Using the SMART Method Yunike Andrayani; Muslimin B; Annafi Franz
TEPIAN Vol 4 No 1 (2023): March 2023
Publisher : Politeknik Pertanian Negeri Samarinda

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (640.597 KB) | DOI: 10.51967/tepian.v4i1.1644

Abstract

Indonesia is one of the developing countries which has characteristics in one sector in agriculture, which is one source of income and improves the economy of farmers, but farmers in processing agricultural land are still not maximized as a means of productive plant land. This productive land requires a technology that can assist in land selection, the purpose of this research is to produce a Decision Support System for the Selection of Productive Land in Corn Plants Using a Web-Based Smart Method, and the author wants to implement the Smart method into the selection of productive land for corn plants that is safe in this system requires data that includes data criteria and alternatives. The results of the data are processed using intelligent methods so that it will produce alternative recommendations that have the highest value. this research can help coordinators of agricultural extension centers assist farmers in managing land to be productive.
Perancangan Aplikasi Digital Farming Untuk Menentukan Replanting Tanaman Kelapa Sawit Menggunakan Metode TOPSIS Dan SAW Muslimin B; Suci Ramadhani; Suswanto Suswanto; Yunike Andrayani; Puput Misliyana; Medi Taruk
Jurnal Rekayasa Teknologi Informasi (JURTI) Vol 7, No 1 (2023): Jurnal Rekayasa Teknologi Informasi (JURTI)
Publisher : Universitas Mulawarman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30872/jurti.v7i1.9264

Abstract

Tanaman kelapa sawit merupakan salah satu komoditas pertanian unggul wilayah Kalimantan Timur yang menghasilkan minyak maupun bahan bakar. Perkembangan dan efektifitas produksi hasil panen tanaman kelapa sawit dipengaruhi beberapa faktor seperti pemeliharaan, pemupukan, kualitas bibit unggul, identifikasi penyakit dan hama. Pengelolaan dan produktivitas lahan perkebunan secara terus menerus maka dibutuhkan proses penanaman kembali tanaman yang kurang produktif. Replanting merupakan teknik peremajaan tanaman kelapa sawit yang kurang produktif menggunakan parameter dan kriteria penilaian. Penelitian ini bertujuan untuk merancang aplikasi digital farming untuk menentukan kelayakan tanaman kelapa sawit yang dapat dilakukan replanting. Aplikasi digital farming merupakan teknologi informasi dan komunikasi(TIK) berbasis cerdas yang dapat menentukan tanaman yang layak untuk di replanting. Manfaat replanting adalah untuk pemenuhan regenerasi tanaman baru kelapa sawit pada suatu kebun yang khususnya di kelola oleh petani. Penelitian ini menerapkan pemodelan/algoritma perbandingan metode Topsis dan SAW dengan berbasis web. Metode Topsis dan SAW merupakan salah satu teknik pengukuran objek tanaman berdasarkan kepentingan kriteria, evaluasi alternatif penilaian, kalkulasi matrik, sehingga menghasilkan rangking tanaman yang layak dilakukan proses replanting. Perbandingan metode Topsis dan SAW dapat mengukur tingkat akurasi keputusan berdasarkan data yang dikelola dan di analisis pakar pertanian. Perbandingan model dan implementasi aplikasi digital farming maka dapat membantu petani pakar pertanian dalam melakukan evaluasi dan monitoring kelayakan tanaman kelapa sawit yang akan di replanting. Untuk jangka panjang maka dapat membantu petani meningkatkan produktivitas hasil panen serta ketersediaan tanaman dan lahan produktif.
Decision Support System for Selection of the Superior Mango Seeds Using Web-based Analytical Hierarchy Process (AHP) Hybrid Simple Additive Weighting (SAW) Method Noviana; Muslimin B; Suci Ramadhani
TEPIAN Vol. 3 No. 2 (2022): June 2022
Publisher : Politeknik Pertanian Negeri Samarinda

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51967/tepian.v3i2.852

Abstract

Indonesia is a horticultural country that agricultural production, one of which is mango production. Mango (Mangifera indica L) is one of the leading horticultural commodities in Indonesia. The use of high-quality seeds has made an impact influence on the productivity of farming, to increase the productivity of farming, it is very necessary to provide superior seeds for farmers so that farmers can increase yields and quality of production. With so many manga seeds available, a Decision Support System is needed or often called a Decision Support System (DSS). DSS is a model-based system consisting of procedures in processing and considerations to assist farmers (users) in making decisions on the selection of high-quality manga seeds. In this research, the method used is the Analytical Hierarchy Process (AHP) in searching for the weighting criteria and the Simple Additive Weighting (SAW) method in performing alternative rankings. The results of this study are to make it easier for farmers and the community in choosing superior manga seeds.
Web-Based Geographic Information System of Livable House in Kandolo Village Rahmawati; Husmul Beze; Muslimin B
TEPIAN Vol. 3 No. 4 (2022): December 2022
Publisher : Politeknik Pertanian Negeri Samarinda

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51967/tepian.v3i4.1417

Abstract

A livable house is abbreviated as the feasibility of a residential house which can be measured from 2 aspects, namely the physical quality of the house and the quality of house facilities. The physical quality of the residential house is measured by 3 variables, namely the type of roof, the type of wall, and the type of floor, while the quality of the housing facilities is measured by 2 variables, namely the source of lighting and the availability of toilet facilities. In this study, the authors use the prototype method using data analysis and system design. This web-based geographic information system for livable houses in Kandolo Village aims to assist in the data collection process for livable houses in Kandolo Village. The results of this study 257 house data have been entered, of which 247 houses are suitable for livable on, 6 houses that are less suitable for livable on, and 4 houses that are not suitable for livable on. For visitors, this system functions to select houses that are livable by looking at several registered pins, then the system will take the resident data detail page. Then in the detail section of citizen data, there will be some resident data, photos of houses, and routes to their destination. From the application trial results, the author conducted a black box test with 11 test class items and respondent tests for direct users at the Kandolo Village Office where the features are used to well and are accepted among the community.
Decision Support System for Selection of Productive Land in Corn Using the SMART Method Yunike Andrayani; Muslimin B; Annafi Franz
TEPIAN Vol. 4 No. 1 (2023): March 2023
Publisher : Politeknik Pertanian Negeri Samarinda

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51967/tepian.v4i1.1644

Abstract

Indonesia is one of the developing countries which has characteristics in one sector in agriculture, which is one source of income and improves the economy of farmers, but farmers in processing agricultural land are still not maximized as a means of productive plant land. This productive land requires a technology that can assist in land selection, the purpose of this research is to produce a Decision Support System for the Selection of Productive Land in Corn Plants Using a Web-Based Smart Method, and the author wants to implement the Smart method into the selection of productive land for corn plants that is safe in this system requires data that includes data criteria and alternatives. The results of the data are processed using intelligent methods so that it will produce alternative recommendations that have the highest value. this research can help coordinators of agricultural extension centers assist farmers in managing land to be productive.
Perbandingan Metode K-Nearest Neighbors dan Naïve Bayes Classifier Pada Klasifikasi Status Gizi Balita di Puskesmas Muara Jawa Kota Samarinda Moch. Rizky Yuliansyah; B, Muslimin; Franz, Annafi
Adopsi Teknologi dan Sistem Informasi (ATASI) Vol. 1 No. 1 (2022): Adopsi Teknologi dan Sistem Informasi (ATASI)
Publisher : Mulawarman University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30872/atasi.v1i1.25

Abstract

Klasifikasi adalah salah satu pembelajaran yang paling umum di dalam data mining. Klasifikasi dapat didefinisikan sebagai bentuk dari analisis data yang digunakan untuk mengekstrak model yang akan digunakan untuk memprediksi label kelas. Status Gizi adalah ukuran keberhasilan dalam pemenuhan nutrisi untuk anak yang diindikasikan oleh berat badan dan tinggi badan anak. Status gizi juga dapat didefinisikan sebagai status kesehatan yang dihasilkan oleh keseimbangan antara kebutuhan dan masukan nutrisi. Metode K-Nearest Neighbors (KNN) merupakan sebuah metode untuk melakukan klasifikasi berdasarkan kedekatan lokasi (jarak) suatu data dengan data yang lain. Prinsip kerja K-Nearest Neighbors (KNN) adalah mencari jarak terdekat antara data yang dievaluasi dengan (K) tetangga terdekatnya dalam data pelatihan. Metode Naïve Bayes Classifier (NBC) merupakan sebuah metode klasifikasi yang memanfaatkan teori probabilitas untuk memprediksi probabilitas di masa depan berdasarkan pengalaman di masa sebelumnya. Adapun tujuan dari penelitian ini adalah untuk melakukan perbandingan hasil Klasifikasi Status Gizi Balita dengan menggunakan metode K-Nearest Neighbors dan Naïve Bayes Classifier. Dari perbandingan performa antara metode K-Nearest Neighbors dan Naïve Bayes Classifier menggunakan f1 score sebagai patokan utama performa klasifikasi. Didapatkan hasil bahwa metode K-Nearest Neighbors unggul pada f1 score dengan selisih cukup besar yakni 13,42 %. Sehingga, dapat disimpulkan bahwa pada masalah klasifikasi status gizi balita metode K-Nearest Neighbors mengungguli Naïve Bayes Classifier.
Sekolah Pintar Berbasis Teknologi Informasi pada Madrasah Aliyah Muslimin Indonesia Center Samarinda B, Muslimin; Ramadhani, Suci; Imron; Satria, Bagus; Rudito
Jurnal ETAM Vol. 4 No. 2 (2024): JUNE
Publisher : Politeknik Negeri Samarinda

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46964/etam.v4i2.734

Abstract

Sekolah pintar merupakan inovasi institusi pendidikan untuk mendukung proses pembelajaran berbasis teknologi Informasi dalam upaya menghadapi tantangan era digital. Kegiatan pembelajaran berbasis teknologi informasi pada Madrasah Aliyah Muslimin Indonesia Center(MIC) Samarinda bertujuan untuk memberikan pelatihan dan pemahaman kepada siswa tentang pengetahuan dan keterampilan untuk menghadapi transformasi perkembangan proses pembelajaran saat ini. Memberikan pemahaman kepada guru dalam menggunakan berbagai platform seperti penggunaan media komputer, perangkat mobile, perangkat lunak, dan aplikasi pendukung kegiatan pembelajaran berbasis teknologi informasi. Siswa dan guru dapat menambah keterampilan teknis seperti penggunaan internet sehat, pengolahan data, etika penggunaan teknologi, keamanan informasi dan media pendukung pembelajaran lainnya. Beberapa permasalahan yang dihadapi oleh guru maupun siswa adalah kurangnya pemahaman dalam menggunakan media dan platform yang mendukung proses pembelajaran berbasis teknologi informasi pada Madrasah Aliyah Muslimin Islamic Center(MIC) Samarinda. Dalam menghadapi permasalahan tersebut kami akan melakukan kegiatan workshop dalam bentuk pelatihan kepada guru dan siswa dalam upaya mengintegrasikan pembelajaran berbasis teknologi informasi dengan kurikulum sekolah, serta pendekatan yang seimbang antara penggunaan teknologi informasi dan metode pembelajaran secara tradisional.
Random Forest Analysis In Classifying Orange Quality Data Suci Ramadhani; Muslimin B; Ida Maratul Khamidah
Jurnal Info Sains : Informatika dan Sains Vol. 14 No. 02 (2024): Informatika dan Sains , Edition, June 2024
Publisher : SEAN Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54209/infosains.v14i02.4420

Abstract

The quality of oranges is important to determine selling value. However, citrus quality assessments are often subjective and inconsistent, which can impact consumer satisfaction and market efficiency. In the agricultural industry, especially in citrus commodities, there are difficulties in classifying fruit quality accurately and efficiently, which has an impact on the assessment and determination of market prices. Given the importance of citrus quality in the agricultural and food industries, there is an urgent need for objective and efficient methods for classifying citrus quality. Inappropriate classification can cause economic losses for farmers and distributors, as well as reduce consumer satisfaction with product quality. As a solution, this research proposes the use of the Random Forest method to classify orange quality data. The method used in this research involved collecting orange quality data, including characteristics such as color, texture, and size. This data is then analyzed using the Random Forest algorithm. The Random Forest method is used to process orange quality data, by utilizing features such as color, size and skin texture. This model is trained using historical data to predict fruit quality. The research results show that the Random Forest method successfully classifies citrus quality data with high accuracy, demonstrating its potential as an effective tool for future citrus quality assessment by proving its effectiveness in supporting decisions in the agricultural sector.
Perancangan SPK Dalam Penentuan Kelayakan Perpanjangan Kontrak Kerja Karyawan PT.WBL Devisi Operasional Menggunakan Metode Profile Matching B, Muslimin; Rowa, Heruzulkifli
Jurnal Sistem Informasi dan Komputer Terapan Indonesia (JSIKTI) Vol 3 No 2 (2020): December
Publisher : INFOTEKS (Information Technology, Computer and Sciences)

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (865.085 KB) | DOI: 10.33173/jsikti.89

Abstract

Private companies currently tend to implement contract systems in all divisions and subdivisions. PT.Wulandari Bangun Laksana(WBL)/ Balikpapan Super Block(BSB) operational scope of the implementation of the contract recruitment system up to several times the contract extension so that the employee is eligible to be appointed as permanent employees. During the extension of the work contract, the operational supervisory supervisor carries out an analysis process in determining the eligibility of employees to be accepted as permanent employees. In order to produce the expected assessment, a mechanism and modeling process is needed that can be processed by a system and evaluation and analysis process carried out by the operational supervisor. This study aims to build a decision support system in determining the feasibility of extending the PT.WBL/Balikpapan Super Block(BSB) employee contract based on good and effective performance objectively using the profile matching method. Decision support system is a technique for using the system in managing the evaluation process by the decision maker (supervisor). The implementation of the profile matching method is the application of a method that can handle the appraisal process based on evaluation of criteria and the value of preferences towards employee alternatives. The data processed is employee data in the scope of the operational division Balikapan Super Block(BSB). The results of the evaluation carried out produce an alternative weight of employees, which can be used as consideration in making decisions on the feasibility of extending the work contract or being appointed as permanent employees. Based on the evaluation process of the criteria and alternatives carried out, it is expected to produce decision values ​​and modeling processes with high accuracy values.
Co-Authors Afak, Richa Rachmawati Andarias Liku annafi franz, annafi Annafi’ Franz Bin Idris, Nasruddin Eko Junirianto Husmul Beze, Husmul Ida Maratul Khamidah Imron Jamal Jamal Jamal Jamal Karim, Syafei Maria, Eny Mauko, Arfan Moch. Rizky Yuliansyah Moch. Rizky Yuliansyah Noviana Nurhuda, Asep Puput Misliyana PUTU SUGIARTAWAN Rachmadani, Budi Racmadhani, Budi Rahmawati Riza Putra, Emil Rowa, Heruzulkifli Rudito Rudito Rudito Selvia Rani Sri Murdhani, I Dewa Ayu Suci Ramadhani Suci Ramadhani Suci Ramadhani Suci Ramadhani Suci Ramadhani, Suci Sumardi . Suswanto Suswanto Taruk, Medi Wahyuni Eka Sari Yuanita Yuanita Yuliyana Yuliyana Yunike Andrayani Yunike Andrayani