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All Journal Jurnal Ilmu Komputer dan Agri-Informatika Scientific Journal of Informatics Jurnal CoreIT
Putra, Fiqhri Mulianda
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Agriculture, Biological Sciences & Forestry Computer Science & IT

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Decision Support System for Evaluation of Peatland Agroecology Suitability in Pineapple Plants Putra, Fiqhri Mulianda; Sitanggang, Imas Sukaesih; Sobir, Sobir
Scientific Journal of Informatics Vol 7, No 1 (2020): May 2020
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v7i1.23819

Abstract

A Pineapple (Ananas comosus (L.) Merr.) It is one of the leading commodities in the Indonesian horticultural sub-sector. Based on data from PDSIP in the last 5 years the development of pineapple production has increased but not too high as well as the harvested area. One of the areas that cultivate pineapple plants in Riau Province is Kampar Regency. Its production in 2015 was 8,482 tons, down from 20,046 tons in 2013. However, this amount is not optimal considering the area in Kampar Regency is still large enough for pineapple cultivation. Kampar District has a potential peatland of around 191,363 ha. About half of the area is thin peat, while the rest varies from moderate to deep peat. The success or failure of peatland management for cultivated land is highly dependent on the condition of its characteristics and the mastery and scientific understanding of the character of peat. This shows the need to evaluate the carrying capacity of land-based on its suitability so that it can be used as a guide in wise land-use planning. This study aims to create a fuzzy inference system model with Mamdani method in determining the agroecological suitability of peatlands for pineapple plants, this is due to the target class of land suitability parameters based on FAO provisions, namely S1, S2, S3, and N. Based on the obtained model, decision support systems will be developed for the suitability of peatland agroecology for pineapple plants.
Decision Support System for Evaluation of Peatland Agroecology Suitability in Pineapple Plants Putra, Fiqhri Mulianda; Sitanggang, Imas Sukaesih; Sobir, Sobir
Scientific Journal of Informatics Vol 7, No 1 (2020): May 2020
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v7i1.23819

Abstract

A Pineapple (Ananas comosus (L.) Merr.) It is one of the leading commodities in the Indonesian horticultural sub-sector. Based on data from PDSIP in the last 5 years the development of pineapple production has increased but not too high as well as the harvested area. One of the areas that cultivate pineapple plants in Riau Province is Kampar Regency. Its production in 2015 was 8,482 tons, down from 20,046 tons in 2013. However, this amount is not optimal considering the area in Kampar Regency is still large enough for pineapple cultivation. Kampar District has a potential peatland of around 191,363 ha. About half of the area is thin peat, while the rest varies from moderate to deep peat. The success or failure of peatland management for cultivated land is highly dependent on the condition of its characteristics and the mastery and scientific understanding of the character of peat. This shows the need to evaluate the carrying capacity of land-based on its suitability so that it can be used as a guide in wise land-use planning. This study aims to create a fuzzy inference system model with Mamdani method in determining the agroecological suitability of peatlands for pineapple plants, this is due to the target class of land suitability parameters based on FAO provisions, namely S1, S2, S3, and N. Based on the obtained model, decision support systems will be developed for the suitability of peatland agroecology for pineapple plants.
Penerapan Learning Vector Quantization 3 (LVQ3) untuk Mengidentifikasi Citra Darah Acute Lymphoblastic Leukemia (ALL) dan Acute Myeloid Leukemia (AML) Putra, Fiqhri Mulianda; Syafria, Fadhilah
Jurnal CoreIT: Jurnal Hasil Penelitian Ilmu Komputer dan Teknologi Informasi Vol 4, No 1 (2018): Juni 2018
Publisher : Fakultas Sains dan Teknologi, Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (722.516 KB) | DOI: 10.24014/coreit.v4i1.6124

Abstract

Leukemia merupakan kanker yang terjadi pada sel darah manusia.  Salah satu cara mengenali penyakit leukemia dengan menggunakan teknik pengolahan citra dan metode jaringan syaraf tiruan. Penelitian ini membangun sebuah sistem untuk mengidentifikasi citra darah leukemia jenis Acute Lymphoblastic Leukemia (ALL) dan  Acute Myeloid Leukemia (AML) dengan konsep pengolahan citra yakni ekstraksi ciri warna Hue, Saturation, Value (HSV) dan ekstraksi ciri tekstur Gray Level Co-Occurence Matrix (GLCM) serta klasifikasi Learning Vector Quantization 3 (LVQ3). Data citra pada penelitian terdiri dari 100 data citra leukemia. Pengujian  identifikasi dilakukan terhadap pembagian data latih dan data uji yang berbeda. Sistem mampu mengenali citra ALL dan AML dengan akurasi tertinggi sebesar 100% pada pembagian data latih 90% dan data uji 10% dengan learning rate 0,01; 0,05; 0,09 dan window 0,2; 0,4 dan akurasi rendah sebesar 70% pada pembagian data latih 50% dan data uji 50% dengan learning rate 0,01; 0,05; 0,09 dan window 0,4. Dengan demikian dapat disimpulkan penelitian menggunakan  metode HSV dan GLCM serta LVQ3 mampu mengimplementasikan sebuah sistem identifikasi citra darah leukemia.
Analisis Potensi Lokasi dan Klasifikasi Electronic Data Capture (EDC) pada UMKM BNI Agen46 Putra, Fiqhri Mulianda; Marimin; Sony Hartono Wijaya; Nusantara, Reinaldy Jalu
Jurnal Ilmu Komputer dan Agri-Informatika Vol. 10 No. 2 (2023)
Publisher : Departemen Ilmu Komputer, Institut Pertanian Bogor

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29244/jika.10.2.133-146

Abstract

Dalam era digitalisasi, peran agen-agen bank menjadi semakin penting dalam memberikan layanan keuangan kepada masyarakat. Bank BNI sebagai salah satu bank terkemuka di Indonesia, memiliki jaringan agen yang luas untuk mendekatkan layanan perbankan kepada nasabah. Dalam upaya mengoptimalkan jaringan agennya, Bank BNI melakukan analisis spasial menggunakan metode clustering K-means untuk menentukan lokasi potensial pendirian Agen46 baru di DKI Jakarta. Selain itu, juga dilakukan pembuatan model klasifikasi random forest Agen46 produktif dan non-produktif untuk mengoptimalkan penggunaan mesin EDC dan menghemat biaya operasional. Berdasarkan analisis spasial dengan metode clustering K-means, ditemukan tujuh lokasi potensial untuk pendirian Agen46 baru di DKI Jakarta, yaitu kecamatan Jagakarsa, Makasar, Pesanggrahan, Grogol Petamburan, Taman Sari, Tambora, dan Johar Baru. Model klasifikasi yang dibuat berhasil membedakan Agen46 yang produktif dan non-produktif dengan akurasi yang tinggi. Selain itu, pembuatan model klasifikasi Agen46 menjadi penting dalam mengenali agen-agen yang tidak produktif, sehingga dapat dilakukan antisipasi dan penanggulangan yang cepat untuk memperbaiki efisiensi penggunaan mesin EDC. Hasil analisis prediksi dan model klasifikasi ini diharapkan dapat memberikan panduan dan dasar kebijakan yang lebih baik bagi Bank BNI dalam menentukan lokasi penempatan mesin EDC Agen46 di masa depan. Dengan demikian, diharapkan Bank BNI dapat mempercepat proses pengklasifikasian Agen46, meningkatkan pemanfaatan mesin EDC, dan mengoptimalkan efisiensi biaya terkait dengan agen-agen BNI.
Co-Authors Fadhilah Syafria Imas Sukaesih Sitanggang Marimin , Nusantara, Reinaldy Jalu Sobir Sobir Sony Hartono Wijaya