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All Journal Ngayah: Majalah Aplikasi IPTEKS Jurnal Dedikasi Masyarakat Jurnal Pertanian Agros Building of Informatics, Technology and Science Kesatria : Jurnal Penerapan Sistem Informasi (Komputer dan Manajemen)
Hakim, Irma
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Agriculture, Biological Sciences & Forestry Computer Science & IT Control & Systems Engineering Social Sciences

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DEMONSTRASI PLOT PEMBESARAN KEPITING RAJUNGAN DENGAN TEKNIK BUDIDAYA TAMBAK DI DESA MATTIRO BOMBANG KABUPATEN PANGKEP Hakim, Irma; Syafiuddin, Syafiuddin; Salam, Nur Insana
Ngayah: Majalah Aplikasi IPTEKS Vol 9 No 2 (2018): Ngayah: Majalah Aplikasi IPTEKS
Publisher : Forum Layanan IPTEKS Bagi Masyarakat (FLipMAS) Wilayah Bali

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Abstract

Mattiro Bombang Village Community activities to catch crabs in the sea. The potential of this village is still not well developed so it is very profitable if the community does crab cultivation. Problems that require handling of the increasingly reduced crab catches are the availability of crabs continuously. To anticipate this, it is necessary to apply and touch the right techniques of crab cultivation so that this PPDM activity becomes a pilot stage. PPDM implementation methods are mentoring and demonstration plots. The main work that was obtained by the fishermen's partners was the demonstration plot of rajungan crab cultivation in the pond that was applied starting from the pond construction, pond drying, filling water, place of maintenance, seed selection, seed transport, seed stocking, maintenance, water quality, feeding, growth, pest management disease, and harvest. With the improvement of skills, fisherman partners can produce large rajungan crabs weighing 200 grams per head to meet market needs and be available continuously without expecting catches at sea.
Pemanfaatan Machine Learning dengan Algoritma X-Means untuk Pemetaan Luas Panen, Produktivitas, dan Produksi Padi Hakim, Irma; Rafid, M.; Anggraini, Fitri
Building of Informatics, Technology and Science (BITS) Vol 4 No 3 (2022): December 2022
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v4i3.2654

Abstract

Rice plants are essential for the world, especially Indonesia because it is a rice-producing plant that is useful as a staple food for its people. A decreased harvest area, production, and rice productivity can affect food availability. Therefore, this research aims to classify and map the harvested area, production, and productivity of rice in Indonesia based on each province. The research data used in this paper is data on the harvested area (ha), production (tons), and rice productivity (Ku/ha) by Provinces in Indonesia for 2020-2022 obtained from the Indonesian Central Bureau of Statistics website. In this study, the algorithm used is X-Means Clustering with the help of the Rapid Miner application. The results of this study are in the form of grouping or mapping of harvested area, production, and productivity of rice, divided into 3 (three) regions, including 1. Harvested Area (divided into five groups: Very high Harvested Area consists of 3 provinces, High Harvested Area consists of 1 province, Medium Harvest Area consists of 3 Provinces, Low Harvest Area consists of 8 Provinces, and Very low Harvest Area consists of 19 Provinces 2. Rice Production Area (divided into five groups: Very high rice production consists of 3 provinces, Rice production High rice production consists of 1 province, Medium rice production consists of 3 Provinces, Low rice production consists of 8 Provinces, and Very low rice production consists of 19 Provinces 3. Regions of Rice Productivity (divided into five groups: Very high rice productivity consists of 6 provinces, High Rice Productivity consists of 13 provinces, Medium Rice Productivity consists of 7 Provinces, Low Rice Productivity consists of 4 Provinces, and Very Low Rice Productivity consists of 4 Provinces. This can be information for the Indonesian government, especially for the respective provincial governments, to be able to maintain the harvested area, production, and productivity of rice in Indonesia to remain stabel.
PENINGKATAN PENGETAHUAN KELOMPOK NELAYAN MELALUI PELATIHAN TEKNIK PEMBENIHAN KEPITING RAJUNGAN Hakim, Irma; Nur Insana Salam; Syafiuddin
Jurnal Dedikasi Masyarakat Vol 3 No 2 (2020): Jurnal Dedikasi Masyarakat
Publisher : Universitas Muhammadiyah Parepare

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31850/jdm.v3i2.485

Abstract

Until now, small crab cultivation in fishing ponds has not been well developed in Mattiro Bombang Village. This is because the crab population along Salemo Island and Sabangko Island is decreasing, leaving the need for crab meat for export unmet. The limited availability of natural seeds among fishing groups has resulted in the small crab cultivation business, initiated last year, also experiencing a decline in production. The PPDM team cares about the problems experienced by fishermen. The next step taken by the team is to provide a training program on crab hatchery technology to the Independent Fishermen Group and the Living Together Fishermen Group. Implementation of PPDM activities uses lecture, discussion, and training methods. The developments obtained after the training show the progress of fishermen's knowledge and the very rapid increase in crab production in the last few months. However, various things still have to be implemented to obtain satisfactory results and improve the economic level of fishermen. One thing fishermen can implement is to produce their crab seeds to solve the problem of a shortage of seeds in anticipation of the development of crab enlargement which is expected to accelerate in the next decade.
Implementasi Algoritma Komputasi Linear Regression untuk Optimasi Prediksi Hasil Pertanian Hakim, Irma; Asdi, A; Afriliansyah, Teuku
Kesatria : Jurnal Penerapan Sistem Informasi (Komputer dan Manajemen) Vol 5, No 3 (2024): Edisi Juli
Publisher : LPPM STIKOM Tunas Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/kesatria.v5i3.460

Abstract

The main objective of this research is to implement the Linear Regression computational algorithm to predict crop yields more accurately. The research method includes collecting and analyzing historical data from 10 agricultural samples that include these variables. This data is then used to train a prediction model. The model evaluation used the Mean Squared Error (MSE) and R² score metrics to assess prediction accuracy. The research results show that the Linear Regression model can provide accurate predictions, with prediction results on new data reaching 479.5 kg/ha. Data visualization revealed a significant relationship between environmental variables and crop yields, which supports the validity of the model constructed. The conclusions of this research confirm that implementing computational algorithms can be an effective tool to help farmers make more informed decisions regarding planting times and land management strategies. This not only increases agricultural efficiency and productivity but also helps in reducing uncertainty in crop yields. The implementation of technology using the linear regression algorithm is expected to make a significant contribution to more sustainable and efficient agricultural practices, as well as support increased crop yields in the future.
POTENTIAL FOR USE OF COW URINE LIQUID ORGANIC FERTILIZER AS PLANT NUTRITION IN DRIP HYDROPONIC SYSTEMS Damayanti, Elsa; Syamsia, Syamsia; Rosanna, Rosanna; Hakim, Irma; Mado, Irwan
Agros Journal of Agriculture Science Vol 26, No 2 (2024): Edisi Juli
Publisher : Fakultas Pertanian, Universitas Janabadra

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37159/jpa.v26i2.4192

Abstract

AB mix is an nutrient that is commonly used for hydroponic system, but the price is expensive so it requires an alternatve to using liquid organic fertilizer. Thies research aims to determine the potential of organic fertilizer as a nutrient for the growth of kale, pakchoy and lecttuce in a wick hydroponic system. Thies research was prepared using aspilt plot design (SPD) with the main plot consisting of AB Mix 5 ml/liter (N1), liquid organic fertililizer cow urine  40 ml/liter (N2), liquid organic fertililizer cow urine  80 ml/liter (N3). The subplots consist of kale (T1), pakchoy (T2), and lettuce (T3). The parameters observed were plant height, number of leaves, root length, plant fresh weight. The results of this research show that the nutrients from liquid organic fertilizer from cow unire and AB mix have no significant effect on the growth of kale, pakchoy and lettuce in the drip hydroponic system. A dose of liquid organic fertilizer from cow urine of 80 ml/liter gave the best average results on plant height, number of leaves and fresh wight of kale, pakchoy and lettuce, but the results obtained were lower when compared with the application of AB mix. The dosage of cow urine liquid organic fertilizwer still needs to be increased to produce the best plant growth and production in the drip hydroponic system
Klasifikasi Kualitas Produk Mesin Pertanian Berdasarkan Evaluasi Kinerja Algoritma Random Forest Hakim, Irma; Asdi, Asdi; Lubis, Mhd. Dicky Syahputra; Harahap, Mely Novasari; Bara, Lokot Ridwan Batu
Kesatria : Jurnal Penerapan Sistem Informasi (Komputer dan Manajemen) Vol 6, No 1 (2025): Edisi Januari
Publisher : LPPM STIKOM Tunas Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/kesatria.v6i1.577

Abstract

This study aims to classify product quality in the agricultural industry using the Random Forest algorithm. The data used includes various inspection result parameters, such as dimensions, weight, product color, quality status, defect image, inspection time, temperature, machine speed, and indicator lights. The model is developed to classify products into "good" and "defective" categories, and is evaluated based on accuracy metrics and confusion matrix analysis. The results show that the Random Forest model is able to achieve an accuracy of 85% in classifying product quality. Based on the confusion matrix, the model has a perfect prediction rate for good quality products (100% precision) and several misclassifications in the defect category. Feature importance analysis shows that the parameters of inspection time, machine temperature, and defect image are the most significant factors in determining product quality. This study proves that the Random Forest algorithm can be a reliable tool to support the product quality inspection process in the agricultural industry, with further integration into IoT-based systems, this approach can improve the efficiency of the inspection process, reduce manual errors, and ensure more consistent product quality standards.
Co-Authors Afriliansyah, Teuku Asdi, A Asdi, Asdi Bara, Lokot Ridwan Batu Damayanti, Elsa Fitri Anggraini, Fitri Harahap, Mely Novasari Lubis, Mhd. Dicky Syahputra Mado, Irwan Nur Insana Salam Rafid, M. Rosanna, Rosanna S Syamsia Syafiuddin Syafiuddin Syafiuddin