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All Journal ILKOM Jurnal Ilmiah Jurnal Nasional Komputasi dan Teknologi Informasi Jurnal Tecnoscienza
Botutihe, Marniyati Husain
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MODEL NEURAL NETWORK BERBASIS FORWARD SELECTION UNTUK PREDIKSI JUMLAH PRODUKSI MINYAK KELAPA Botutihe, Marniyati Husain
ILKOM Jurnal Ilmiah Vol 9, No 3 (2017)
Publisher : Program Studi Teknik Informatika Fakultas Ilmu Komputer Univeristas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1111.521 KB)

Abstract

Sulitnya memprediksi jumlah produksi dimasa datang sehingga permintaan komsumen seringkali tidak terpenuhi dengan baik. Tujuan penelitian ini adalah untuk membuat model prediksi jumlah produksi minyak kelapa menggunakan metode neural network berbasis forward selection, untuk mengetahui jumlah produksi di masa yang akan datang dengan tingkat error yang lebih rendah. Model yang dipilih berdasarkan nilai root mean square error (RMSE) terkecil yang diperoleh dari hasil pengujian. Hasil prediksi jumlah produksi minyak kelapa berdasarkan penelitian yang telah dilakukan sebelumnya dengan hasil niali aktual jumlah produksi minyak kelapa januari 2015, nilai tersebut berdasarkan hasil produksi yang telah terjadi. Dengan rata – rata presentase yang diperoleh yaitu 91.01%.
CASE BASED REASONING METHOD UNTUK SISTEM PAKAR DIAGNOSA PENYAKIT SAPI Muzakkir, Irvan; Botutihe, Marniyati Husain
ILKOM Jurnal Ilmiah Vol 12, No 1 (2020)
Publisher : Teknik Informatika Fakultas Ilmu Komputer Univeristas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1863.41 KB) | DOI: 10.33096/ilkom.v12i1.506.25-31

Abstract

This study discusses about the Application of Case Based Reasoning (CBR) Method for Expert Systems in Diagnosing Cattle Disease. Beginning with data collection by consulting experts in the Department of Agriculture in Animal Health, Pohuwato Regency. The data obtained in the form of data names of disease and symptom data. The data is obtained based on the steps of the CBR method calculation in order to obtain the results of the diagnosis and the solution provided for handling the disease. Researcher have analyzed and create program listings to build a system that will be used by farmers. Based on CBR calculations Scours case which has the lowest weight is 0.09 while the highest weight is owned by the Pink Eye case 1. In this process provides a solution to the similarity of the case weight from the old case to the new higher case. In the case of Pink Eye having a higher weight and positive exposure to pink eye disease, the solution given is the provision of anti-allergic, anti-biotic and vitamin. Based on the results obtained, it can be concluded that the application of the CBR method is good for using cattle disease and is very helpful for farmers in dealing with cattle disease. 
Sistem Pakar Diagnosa Penyakit Tanaman Singkong Menggunakan Metode Case Based Reasoning Botutihe, Marniyati H.
JURNAL TECNOSCIENZA Vol. 3 No. 1 (2018): TECNOSCIENZA
Publisher : JURNAL TECNOSCIENZA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51158/jvqwep60

Abstract

Mengidentifikasi penyakit tanaman singkong dapat diketahui dari gejala-gejala dan perubahan warna yang muncul. Tanaman yang terkena penyakit dengan gejala yang berbeda membutuhkan penanganan yang berbeda. Oleh karena itu penulis melakukan suatu penelitian tentang Sistem Pakar Diagnosa Penyakit Tanaman Singkong, tujuannya adalah mengidentifikasi penyebab, gejala, dan cara penanganannya pada pengguna dengan memperhatikan aturan-aturan, serta memberikan solusi penanganannya, agar kedepannya dapat digunakan untuk meminimalisisr atau memperkecil resiko penyakit pada Tanaman Singkong. Peneliti mencoba membantu permasalahan tersebut di atas dengan membuatkan suatu sistem dengan metode Case Based Reasoning (CBR) yaitu salah satu metode yang dapat melakukan penalaran atau memecahkan permasalahan berdasarkan kasus yang telah ada sebagai solusi masalah baru. Sistem ini dibuat dengan memakai Bahasa Pemrograman PHP, Database MySQL, serta penggunaan Aplikasi Deamweaver dan Photoshop. Berdasarkan hasil dari pengujian white box dapat ditarik kesimpulan bahwa sistem pendukung keputusan ini bebas dari kesalahan program dengan total Cyclomatic Complexity = 4 dan Region = 4. Kata kunci: Penyakit Tanaman Singkong, CBR, PHP, MySQL, Dreamweaver
Metode Case Based Reasoning untuk Sistem Pakar Diagnosa Penyakit Akibat Serangan Hama pada Tanaman Padi Botutihe, Marniyati H.; Bahrin, Bahrin; Arbabu, Rahmat
Jurnal Nasional Komputasi dan Teknologi Informasi (JNKTI) Vol 8, No 6 (2025): Desember 2025
Publisher : Program Studi Teknik Komputer, Fakultas Teknik. Universitas Serambi Mekkah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32672/jnkti.v8i6.10207

Abstract

Abstrak - Tanaman padi merupakan salah satu jenis pangan utama di dunia setelah gandum dan jagung, dengan peran signifikan dalam pemenuhan kebutuhan karbohidrat hampir setengah populasi global. Namun, berbagai hama seperti serangga, hewan, dan mikroorganisme dapat merusak pertumbuhan dan produksi tanaman padi. Penelitian ini bertujuan untuk merancang sistem pakar yang menggunakan metode Case Based Reasoning (CBR) untuk mendiagnosis penyakit akibat serangan hama pada tanaman padi, Tujuan utamanya adalah Memperoleh hasil penerapan metode Case Based Reasoning pada sistem pakar diagnosa penyakit serangan hama pada  tanaman padi. Metode CBR memungkinkan diagnosis dengan membandingkan gejala baru terhadap kasus sebelumnya yang terdokumentasi, sehingga memanfaatkan pengalaman untuk menyelesaikan masalah baru. Hasil implementasi sistem ini menunjukkan bahwa sistem berfungsi sesuai harapan melalui pengujian White Box dengan Cyclomatic Complexity sebesar 9, serta pengujian Black Box. Dengan demikian, sistem pakar tersebut layak digunakan untuk membantu petani dalam mengidentifikasi penyakit pada tanaman padi dan mengambil tindakan yang tepat.Kata kunci : Padi; hama; Sistem Pakar; Case Based Reasoning; CBR; Abstract - Rice is one of the world’s main food crops after wheat and corn, playing a significant role in fulfilling the carbohydrate needs of nearly half of the global population. However, various pests such as insects, animals, and microorganisms can damage the growth and production of rice plants. This study aims to design an expert system that uses the Case-Based Reasoning (CBR) method to diagnose diseases caused by pest attacks on rice plants. The main objective is to obtain the results of applying the Case-Based Reasoning method in an expert system for diagnosing pest-attack diseases in rice plants. The CBR method enables diagnosis by comparing new symptoms with previously documented cases, thus utilizing past experiences to solve new problems. The results of implementing this system indicate that it functions as expected through White Box testing with a Cyclomatic Complexity of 9, as well as Black Box testing. Therefore, the expert system is considered feasible to assist farmers in identifying diseases in rice plants and taking appropriate action.Keywords: Rice; Pests; Expert Systems; Case Based Reasoning; CBR;
Predicting the success of the government’s program of lomaya (Regional PKH) in reducing poverty Sulaehani, Ruhmi; Botutihe, Marniyati Husain
ILKOM Jurnal Ilmiah Vol 14, No 3 (2022)
Publisher : Prodi Teknik Informatika FIK Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v14i3.1149.323-328

Abstract

Poverty reduction is one indicator of the success of development. The form of support from the Pohuwato Regency Government through the Social Service is to organize PKH-D, which is known as LOMAYA. It is one of the implementations of the Community Movement Towards Independent Prosperity (Gerakan Masyarakat Menuju Sejahtera Mandiri). This research was conducted to assist the government in predicting the level of development success indicated by the satisfaction of beneficiaries of lomaya. The method employed was the Naïve Bayes method and forward feature selection. The research data was obtained from a survey of lomaya beneficiaries in the last two years. The accuracy result obtained using the Naïve Bayes algorithm was 94.19%, while Naïve Bayes with the Forward Selection feature was only 94.03%. Therefore, the Naïve Bayes algorithm method is better than the Forward Selection based Naïve Bayes algorithm. Forward selection does not improve accuracy because the selection process causes many attributes to be discarded because they are considered irrelevant. This happened because of the inaccuracy of the data after being selected for its attributes using the Forward Selection feature resulting 1 attribute  only as a determinant.
Case Based Reasoning Method untuk Sistem Pakar Diagnosa Penyakit Sapi Muzakkir, Irvan; Botutihe, Marniyati Husain
ILKOM Jurnal Ilmiah Vol 12, No 1 (2020)
Publisher : Prodi Teknik Informatika FIK Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v12i1.506.25-31

Abstract

This study discusses about the Application of Case Based Reasoning (CBR) Method for Expert Systems in Diagnosing Cattle Disease. Beginning with data collection by consulting experts in the Department of Agriculture in Animal Health, Pohuwato Regency. The data obtained in the form of data names of disease and symptom data. The data is obtained based on the steps of the CBR method calculation in order to obtain the results of the diagnosis and the solution provided for handling the disease. Researcher have analyzed and create program listings to build a system that will be used by farmers. Based on CBR calculations Scours case which has the lowest weight is 0.09 while the highest weight is owned by the Pink Eye case 1. In this process provides a solution to the similarity of the case weight from the old case to the new higher case. In the case of Pink Eye having a higher weight and positive exposure to pink eye disease, the solution given is the provision of anti-allergic, anti-biotic and vitamin. Based on the results obtained, it can be concluded that the application of the CBR method is good for using cattle disease and is very helpful for farmers in dealing with cattle disease. 
Visitor satisfaction prediction of the 'Pantai Pohon Cinta' beach tourism using the backpropagation algorithm with particle swarm optimization feature selection Riadi, Annahl; Botutihe, Marniyati Husain
ILKOM Jurnal Ilmiah Vol 13, No 2 (2021)
Publisher : Prodi Teknik Informatika FIK Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v13i2.791.117-124

Abstract

This study focuses on the visitors of Pohon Cinta beach tourist area. This beach is one of the potential tourism objects in Pohuwato Regency. The main problem that frequently occurs is that many visitors cannot directly convey their impression when visiting and enjoying the beauty of the Pohon Cinta beach. The government needs to know the level of visitor satisfaction to attempt to improve and develop the Pohon Cinta beach tourist attraction. Thus, to solve the problem above, a method that can help predict visitor satisfaction is needed. This study aims to measure visitor satisfaction through predictions using the Backpropagation algorithm and PSO feature selection to assist the government in developing tourism potential in Pohuwato Regency. The method used is the backpropagation algorithm for prediction and Particle Swarm Optimization which is considered effective in overcoming optimization problems. This algorithm is considered capable of solving problems in the backpropagation algorithm. The accuracy value of the backpropagation algorithm model is 84.67%, the accuracy value of the PSO-based backpropagation algorithm model is 85.00%, and the difference in accuracy is 0.33. The results of the application of the Backpropagation algorithm and Particle Swarm Optimization can increase the predictive accuracy value of visitor satisfaction at the Cinta Tree Beach tourist attraction.
MODEL NEURAL NETWORK BERBASIS FORWARD SELECTION UNTUK PREDIKSI JUMLAH PRODUKSI MINYAK KELAPA Botutihe, Marniyati Husain
ILKOM Jurnal Ilmiah Vol 9, No 3 (2017)
Publisher : Prodi Teknik Informatika FIK Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v9i3.149.239-243

Abstract

Sulitnya memprediksi jumlah produksi dimasa datang sehingga permintaan komsumen seringkali tidak terpenuhi dengan baik. Tujuan penelitian ini adalah untuk membuat model prediksi jumlah produksi minyak kelapa menggunakan metode neural network berbasis forward selection, untuk mengetahui jumlah produksi di masa yang akan datang dengan tingkat error yang lebih rendah. Model yang dipilih berdasarkan nilai root mean square error (RMSE) terkecil yang diperoleh dari hasil pengujian. Hasil prediksi jumlah produksi minyak kelapa berdasarkan penelitian yang telah dilakukan sebelumnya dengan hasil niali aktual jumlah produksi minyak kelapa januari 2015, nilai tersebut berdasarkan hasil produksi yang telah terjadi. Dengan rata – rata presentase yang diperoleh yaitu 91.01%.
Particle Swarm optimization-based Neural Network method for predicting satisfaction of recipients of internet data quota assistance from the ministry of education and culture Riadi, Annahl; Muzakkir, Irvan; Botutihe, Marniyati H.
ILKOM Jurnal Ilmiah Vol 14, No 1 (2022)
Publisher : Prodi Teknik Informatika FIK Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v14i1.1094.52-56

Abstract

The free quota assistance program for students and lecturers is an assistance program provided by The Ministry of Education and Culture. This program has been implemented since the spread of the covid-19 pandemic in all regions of Indonesia. This assistance is expected to help students and lecturers carry out online learning caused by the pandemic covid-19. This study aims to predict the satisfaction level of the users so that it can help the government in advancing education. The data processing is carried out using the rapid miner application and the neural network method with particle swarm optimization. From the results of data processing, the accuracy value for the neural network algorithm model is 42.44%, and the accuracy value for the PSO-based neural network algorithm model is 91.86%.
Co-Authors Arbabu, Rahmat Bahrin Bahrin Irvan Muzakkir Irvan Muzakkir Riadi, Annahl Sulaehani, Ruhmi