Article Per Year (5 Year)
p-Index From 2018 - 2023
1.745
P-Index
This Author published in this journals
All Journal Jurnal Rekayasa Elektrika JURNAL NASIONAL TEKNIK ELEKTRO Jurnal Penelitian Pertanian Terapan Agriprima : Journal of Applied Agricultural Sciences Jurnal Agro Industri Perkebunan Applied Technology and Computing Science Journal JISA (Jurnal Informatika dan Sains) Journal of Community Development
Abdul Madjid
Politeknik Negeri Jember
Agriculture, Biological Sciences & Forestry Biochemistry, Genetics & Molecular Biology Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering Nursing Public Health Social Sciences

Published : 9 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 9 Documents
Search

 1 
Penerapan Neural Network untuk Klasifkasi Kerusakan Mutu Tomat Zilvanhisna Emka Fitri; Rizkiyah Rizkiyah; Abdul Madjid; Arizal Mujibtamala Nanda Imron
Jurnal Rekayasa Elektrika Vol 16, No 1 (2020)
Publisher : Universitas Syiah Kuala

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (807.791 KB) | DOI: 10.17529/jre.v16i1.15535

Abstract

The decrease in quality and productivity of tomatoes is caused by high rainfall, bad weather and cultivation so that the tomatoes become rotten, cracked, and spotting occurs. The government is trying to provide training to improve the quality of tomatoes for farmers. However, the training was not effective so the researchers helped create a system that was able to educate farmers in the classification of damage to tomato quality. This system serves to facilitate farmers in recognizing tomato damage thereby reducing the risk of crop failure. In this study, the classification method used is backpropagation with 7 input parameters. The input consists of morphological and texture features. The output of this classification system consists of 3 classes are blossom end rot, fruit cracking and fruit spots caused by bacterial specks. The best accuracy level of the system in classifying tomato quality damage in the training process is 89.04% and testing is 81.11%.
Pemanfaatan Power Sprayer Guna Mengendalikan Hama Kopi di Desa Klungkung Jember Abdul Madjid; Abdurrahman Salim; Anni Nur Aisyah; Zilvanhisna Emka Fitri
Journal of Community Development Vol. 3 No. 1 (2022): August
Publisher : Indonesian Journal Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47134/comdev.v3i1.70

Abstract

Coffee is one of the plantation commodities that are in great demand in Indonesia. Coffee production in East Java is the largest in Indonesia, one of the coffee-producing areas in East Java, namely Jember Regency. Some of the factors causing it, one of them from cultivation techniques and inadequate care and maintenance. In particular, many coffee pests are not handled properly. In addition, there is a factor in the level of technology absorption and the application of farm management as well as a less efficient and effective marketing system which has an impact on the income level of farmers. Therefore, it is necessary to innovate cultivation techniques and maintain coffee plants in order to maintain optimal coffee growth and produce better fruit, so as to increase farmers' income. The microcontroller-based sprayer battery is an innovative sprayer to increase coffee production in Klungkung village. The stages of this service activity start from the stage of preparation and coordination with partners, digging information (literature studies) in compiling counseling and training materials from controlling plant pest organisms, especially coffee from spraying techniques according to SOPs, coffee production management, to the coffee marketing system. The results of this dedication is the farmer of Klungkung village get benefits in good coffee cultivation techniques and in spraying pests using Power Sprayer technology.
Comparison of Neural Network Methods for Classification of Banana Varieties (Musa paradiasaca) Zilvanhisna Emka Fitri; Wildan Bakti Nugroho; Abdul Madjid; Arizal Mujibtamala Nanda Imron
Jurnal Rekayasa Elektrika Vol 17, No 2 (2021)
Publisher : Universitas Syiah Kuala

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1.311 KB) | DOI: 10.17529/jre.v17i2.20806

Abstract

Every region in Indonesia has a very large diversity of banana species, but no system records information about the characteristics of banana varieties. The purpose of this research is to make an encyclopedia of banana types that can be used for learning by classifying banana varieties using banana images. This banana variety classification system uses image processing techniques and artificial neural network methods as classification methods.The varieties of bananas used are pisang merah, pisang pisang mas kirana, pisang klutuk, pisang raja and pisang cavendis. The parameters used are color features (Red, Green, and Blue) and shape features (area, perimeter, diameter, and length of fruit). The intelligent system used is the Backpropagation method and the Radial Basis Function Neural Network. The results showed that both methods were able to classify banana varieties with an accuracy rate of 98% for Backpropagation and 100% for the Radial Basis Function Neural Network.
Comparison of Classification for Grading Red Dragon Fruit (Hylocereus Costaricensis) Zilvanhisna Emka Fitri; Ari Baskara; Abdul Madjid; Arizal Mujibtamala Nanda Imron
JURNAL NASIONAL TEKNIK ELEKTRO Vol 11, No 1: March 2022
Publisher : Jurusan Teknik Elektro Universitas Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (517.489 KB) | DOI: 10.25077/jnte.v11n1.899.2022

Abstract

Pitaya is another name for dragon fruit which is currently a popular fruit, especially in Indonesia. One of the problems related to determining the quality of dragon fruit is the postharvest sorting and grading process. In general, farmers determine the grading system by measuring the weight or just looking at the size of the fruit, of course, this raises differences in grading perceptions so that it is not by SNI. This research is a development of previous research, but we changed the type of dragon fruit from white dragon fruit (Hylocereus undatus) to red dragon fruit (Hylocereus costaricensis). We also adapted the image processing and classification methods in previous studies and then compared them with other classification methods. The number of images in the training data is 216, and the number of images in the testing data is 75. The comparison of the accuracy of the three classification methods is 84% for the KNN method, 85.33% for the Naive Bayes method, and 86.67% for the Backpropagation method. So that the backpropagation method is the best classification method in classifying the quality grading of red dragon fruit. The network architecture used is 4, 8, 3 with a learning rate of 0.3 so that the training accuracy is 98.61% and the testing accuracy is 86.67%.
Pengaruh Penggunaan Beberapa Stimulansia Terhadap Produksi Berberapa Klon Karet (Hevea brasilliensis Muell Arg.) Eka Renitasari; Titien Fatimah; Abdul Madjid
Agriprima : Journal of Applied Agricultural Sciences Vol 3 No 1 (2019): MARET
Publisher : Politeknik Negeri Jember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25047/agriprima.v3i1.132

Abstract

Salah satu penunjang produksi tanaman karet dalam menghasilkan getah lateks adalah pengaplikasian stimulansia. Penelitian ini bertujuan untuk mengetahui interaksi antara klon karet dengan stimulansia terhadap produksi tanaman karet. Penelitian ini dilakukan di PTP N XII Kebun renteng Ajung Kabupaten Jember pada 26 desember 2017 – 15 Januari 2018. Penelitian ini mengguunakan Rancangan Acak Kelompok(RAK) dengan dua faktor. Faktor pertama adalah faktor klon menggunakan 4 taraf yaitu, taraf K1 (PB 260), K2 (GT 1), K3 (RRIC 100), K4 (BPM 24). Faktor kedua adalah stimulansia dengan 3 taraf yaitu, S1 (Amcotrel 10 PA), S2 (karet full), S3 (non stimulansia). Hasil penelitian menunjukkan bahwa interaksi yang baik ditunjukkan oleh kombinasi perlakuan antara klon RRIC 100 dengan stimulansia karet full dan kombinasi perlakuan GT 1 dengan stimulansia amcotrel 10 PA. Namun, hasil produksi getah lateks menunjukkan bahwa perlakuan kontrol lebih tinggi dari semua perlakuan stimulansia.
Application of Feature Selection for Identification of Cucumber Leaf Diseases (Cucumis sativa L.) Lalitya Nindita Sahenda; Ahmad Aris Ubaidillah; Zilvanhisna Emka Fitri; Abdul Madjid; Arizal Mujibtamala Nanda Imron
JISA(Jurnal Informatika dan Sains) Vol 4, No 2 (2021): JISA(Jurnal Informatika dan Sains)
Publisher : Universitas Trilogi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31326/jisa.v4i2.1046

Abstract

According to data from BPS Kabupaten Jember, the amount of cucumber production fluctuated from 2013 to 2017. Some literature also mentions that one of the causes of the amount of cucumber production is disease attacks on these plants. Most of the cucumber plant diseases found in the leaf area such as downy mildew and powdery mildew which are both caused by fungi (fungal diseases). So far, farmers check cucumber plant diseases manually, so there is a lack of accuracy in determining cucumber plant diseases. To help farmers, a computer vision system that is able to identify cucumber diseases automatically will have an impact on the speed and accuracy of handling cucumber plant diseases. This research used 90 training data consisting of 30 healthy leaf data, 30 powdery mildew leaf data and 30 downy mildew leaf data. while for the test data as many as 30 data consisting of 10 data in each class. To get suitable parameters, a feature selection process is carried out on color features and texture features so that suitable parameters are obtained, namely: red color features, texture features consisting of contrast, Inverse Different Moment (IDM) and correlation. The K-Nearest Neighbor classification method is able to classify diseases on cucumber leaves (Cucumis sativa L.) with a training accuracy of 90% and a test accuracy of 76.67% using a variation of the value of K = 7. 
Red Dragon Fruit (Hylocereus costaricensis) Ripeness Color Classification by Naïve Bayes Algorithm Zilvanhisna Emka Fitri; Mega Silvia; Abdul Madjid; Arizal Mujibtamala Nanda Imron; Lalitya Nindita Sahenda
TEKNOLOGI DITERAPKAN DAN JURNAL SAINS KOMPUTER Vol 5 No 1 (2022): June
Publisher : Unusa Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33086/atcsj.v5i1.3690

Abstract

Dragon fruit is a unique fruit that is popular in Indonesia. besides having a sweet taste, this fruit also contains fiber, vitamins and minerals that are good for health. Dinas Pertanian Kabupaten Banyuwangi noted that the total dragon fruit production was 906,511.61 tons and the total productivity was 261.14 Kw/Ha in 2018. This shows that Kabupaten Banyuwangi is one of the largest producers of red dragon fruit in East Java Province. One of the problems in determining the quality of dragon fruit is choosing the harvest time, considering that dragon fruit is a non-climatic fruit. Non-climateric fruit is when we harvest fruit in its raw state, the fruit will never become ripe, so determining the harvest time for dragon fruit is very important. The determination made by paying discoloration and sizes of dragon fruit that is considered less effective. To overcome this, a system was created that was able to determine the level of dragon fruit maturity automatically by utilizing digital image processing techniques and intelligent systems. The parameters used are color features and GLCM texture features using angles 0°, 45°, 90° and 135° These features are parameters in the classification process using the Naïve Bayes method. Naïve bayes is able to classify the level of maturity of red dragon fruit (Hylocereus costaricensis) with an accuracy rate of 87.37%.
PENGARUH JENIS MEDIA PUPUK KANDANG DAN PEMBERIAN PUPUK ORGANIK CAIR DAUN LAMTORO TERHADAP PERTUMBUHAN BIBIT VANILI (Vanilla planifolia) Abdurrahman Salim; Ujang Setyoko; Abdul Madjid; Hasyim Asyari
Jurnal Penelitian Pertanian Terapan Vol 23 No 1 (2023)
Publisher : Politeknik Negeri Lampung.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25181/jppt.v23i1.2306

Abstract

Provision of nutrients is one of the important things in plant cultivation. One of the nutrients that can be obtained from organic fertilizers. Manure is one type of organic fertilizer that can increase soil nutrients. The manure used in the media is cow and goat manure. In addition to manure, the application of Liquid Organic Fertilizer (POC) of lamtoro leaves can also provide nitrogen elements in the soil. The element nitrogen has good benefits for nurseries because it can accelerate vegetative growth in plants. The purpose of this study was to determine the effect of adding manure media by giving lamtoro leaf POC to vanilla seeds. This research was carried out at the Jember State Polytechnic in July – November 2020. This study used a factorial randomized block design with two factors, namely the first factor was 3 types of planting media consisting of control, addition of cow kendang fertilizer and addition of cow kendang fertilizer. Then the second factor was giving lamtoro leaf liquid organic fertilizer with three levels consisting of control, 25%, and 50%.. The results showed that goat manure showed the best results in increasing shoot length, shoot diameter, leaf width and wet weight of vanilla plants. Application of liquid organic fertilizer with a concentration of 50% gave the best results for increasing shoot length, shoot diameter and wet weight of vanilla plants. Meanwhile, the interaction does not show a significant effect. Keywords:Lamtoro Leaves; Manure; POC; Vanilla plant
Karakteristik Agronomi Tanaman Kapas (Gossypium sp.) dan Pengaruhnya terhadap Produksi Kapas Menggunakan Analisis Lintas Virda Fauziah; Ujang Setoko; Abdurrahman Salim; Abdul Madjid
Jurnal Agro Industri Perkebunan Volume 11 Nomor 1 Tahun 2023
Publisher : Politeknik Negeri Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25181/jaip.v11i1.2677

Abstract

The cotton plant is a fiber plant that is commonly used as a raw material for textiles, beauty, and health products. To increase cotton production, the development of superior varieties using plant breeding methods in cross-analysis is necessary. The cross-analysis method is used to determine the agronomic traits that affect cotton production, by selecting yield through several other characteristics related to yield. The aim of this study was to identify which agronomic characters can be used as selection criteria to increase cotton production using cross-analysis. The research was conducted at Politeknik Negeri Jember, and included 12 independent variables and one response variable, namely cotton production. The method used in this study was to perform correlation analysis, cross-analysis, calculate direct and residual contributions, and select agronomic characters that can be used as selection criteria. The results showed that the number of fruit characters had the highest correlation with cotton production (RX9Y = 0.835). Cross-analysis was carried out, and the highest direct effect was found between the number of fruit characters and cotton production (PX9Y = 0.971). The highest direct contribution was found in the character of the number of fruit, which had a total contribution of 98.321% and residue of 1.679%. Therefore, the agronomic character that can be used as a direct selection criterion is the number of fruits.
Co-Authors Abdurrahman Salim Ahmad Aris Ubaidillah Anni Nur Aisyah Ari Baskara Eka Renitasari Hasyim Asyari Imron, Arizal Mujibtamala Nanda Lalitya Nindita Sahenda Mega Silvia Rizkiyah Rizkiyah Titien Fatimah Ujang Setoko Ujang Setyoko Virda Fauziah Wildan Bakti Nugroho Zilvanhisna Emka Fitri