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Implementasi Metode Case-Based Reasoning, Dempster-Shafer, dan Teorema Bayes pada Sistem Pakar Identifikasi Hama dan Penyakit Tanaman Manggis Farha Fitrahul Janah; Meita Arifany; Siti Aisyah; Freza Riana
Jurnal Ilmu Komputer & Agri-Informatika Vol. 8 No. 2 (2021)
Publisher : Departemen Ilmu Komputer - IPB University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29244/jika.8.2.135-144

Manggis mempunyai prospek tinggi dalam skala agribisnis. Berdasarkan data ekspor komoditas pertanian subsektor hortikultura (buah-buahan segar), manggis berada pada peringkat ke-1 pada tahun 2020. Kendala budidaya manggis adalah rendahnya kualitas buah yang tidak layak ekspor yang disebabkan oleh serangan hama dan penyakit. Umumnya, upaya para petani manggis saat menghadapi masalah tersebut adalah berkonsultasi dengan seorang pakar. Namun, ketersediaan dan kehadiran pakar dalam suatu daerah masih sangat terbatas. Sistem pakar menjadi solusi untuk mengidentifikasi hama dan penyakit pada tanaman manggis secara cepat. Sistem pakar dihadirkan sebagai salah satu cara untuk memecahkan permasalahan setelah seorang pakar. Metode case-based reasoning, Dempster-Shafer, dan teorema Bayes merupakan metode yang dapat digunakan pada sistem pakar. Penelitian ini bertujuan untuk membangun sistem pakar berbasis website dalam mengidentifikasi hama dan penyakit yang menyerang tanaman manggis berdasarkan gejala-gejala. Ketiga metode tersebut diterapkan performanya dengan melihat nilai akurasinya. Nilai akurasi didapatkan dari perbandingan hasil jawaban sistem per tiap metode dengan hasil pakar. Dari 30 uji data yang dilakukan secara acak, hasil penelitian ini menunjukkan nilai akurasi yang sama untuk case-based reasoning dan Dempster-Shafer, yaitu sebesar 93%, dan teorema Bayes sebesar 90%. Sistem Pakar yang telah dibangun dapat digunakan oleh petani untuk mengidentifikasi hama dan penyakit pada tanaman manggis.

Application Of The Waterfall Method In The Final Project Guidance Realization Information System Ritzkal; Puspa Putri Amalia; A. Hendri Hendrawan; Freza Riana; Kodarsyah
Jurnal Mantik Vol. 6 No. 2 (2022): August: Manajemen, Teknologi Informatika dan Komunikasi (Mantik)
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar

The thesis guidance process cannot run smoothly and on time if the busyness between lecturers and students causes a lack of suitable time to meet, so this obstacle can make the problems in writing the thesis unable to be solved as soon as possible. In addition, lecturers also find it difficult to monitor their tutored students because there are not a few students who are mentored. The purpose of this research is (a) to build an information system for the realization of the final project by using the waterfall method and (b) to test using a black box. Based on the results and discussion, conclusions can be drawn as follows: (i) Development of an information system for the realization of the final project in the waterfall method using UML design where the design results include System Actors (List Actor), Use Case Diagrams, Activity Diagrams. (ii) System testing using Black Box. The results carried out using black boxes explain that the system created or the module used is running according to what is specified.

SISTEM PAKAR UNTUK MENDIAGNOSIS HAMA DAN PENYAKIT TANAMAN PISANG DENGAN TEOREMA BAYES Lievia Anjhelina Maharani; Gibtha Fitri Laxmi; Freza Riana
Jurnal Ilmiah Teknologi Infomasi Terapan Vol. 8 No. 1 (2021)
Publisher : Universitas Widyatama

Tanaman pisang merupakan salah satu tanaman yang banyak dijumpai di daerah-daerah di Indonesia, padahal di Indonesia sendiri masih minim informasi yang diterima masyarakat tentang hama dan penyakit yang dapat menyerang tanaman pisang dan cara penanggulangannya. Terbatasnya ketersediaan tenaga ahli di berbagai tempat atau wilayah diIndonesia causes the community to be late in dealing with pests and diseases of banana plants, therefore this research creates a website-based expert system to diagnose pests and diseases of banana plants using 11 data consisting of 5 pests and 6 diseases, as well as 48 symptom data that will be processed using the Bayes Theorem method to produce a probability value of a pest or disease based on the symptoms that arise in banana plants. This study resulted in an accuracy value of 86.53%. The accuracy value is obtained from the comparison between expert diagnosis and the system using the Bayes theorem based on 52 tested data. This expert system can help the community in diagnosing banana plant pests and diseases.

PENERAPAN METODE CERTAINTY FACTOR PADA SISTEM PAKAR DIAGNOSIS PENYAKIT DAN HAMA TANAMAN MANGGIS Farha Fitrahul Janah; Gibtha Fitri Laxmi; Freza Riana
Jurnal Ilmiah Teknologi Infomasi Terapan Vol. 8 No. 1 (2021)
Publisher : Universitas Widyatama

Mangosteen is one of the horticultural commodities of Indonesian original tropical fruits and has either high value or prospects for cultivation. One of the obstacles in mangosteen cultivation is the low quality of the fruit caused by pests. In general, one of the efforts to prevent and control the spread of diseases and pests in mangosteen plants is to consult with the expert, however this can be an obstacle due to the limited number of experts in an area. Referring to this problem, in this study, a website-based expert system was created to diagnose diseases and pests in mangosteen plants by applying the Certainty Factor method. There were 5 diseases and 5 pests with 42 symptoms, including 25 symptoms of diseases and 17 symptoms of pests that can be identified by this expert system. The percentage value obtained was 80% for the comparison between expert diagnoses and system diagnoses using the Certainty Factor method. This value was obtained based on the suitability of 50 test data that were randomly generated. This expert system can helps mangosteen farmers and the community in diagnosing diseases and pests in mangosteen plants.

PEMETAAN ZONASI TINGKAT RISIKO COVID-19 MENGGUNAKAN METODE K-MEANS CLUSTER BERBASIS WEBGIS DI KOTA BOGOR Andi Irawan; Erwin Hermawan; Freza Riana
Jurnal Ilmiah Teknologi Infomasi Terapan Vol. 8 No. 2 (2022)
Publisher : Universitas Widyatama

Coronaviruses, also known as COVID-19, is a virus that attacks the respiratory system. This virus is growing very rapidly in transmission from human to human. The distribution rate in Indonesia is quite high, one of the areas exposed to COVID-19 is Bogor City. The city of Bogor occupies a moderately dangerous status, where from March 2020 to August 2021 the number of confirmed positive cases was 35,583 cases, while for patients who were confirmed positive recovered reached 35,062 people, and patients who were confirmed positive died as many as 521 people. From data obtained from the Bogor City Health Office, the daily dynamics of the COVID-19 virus continues to show an increase. The COVID-19 zoning distribution map for the Bogor City area at the kelurahan level is still not available. Therefore, this study utilizes a geographic information system (GIS) within the COVID-19 zone by applying the Kmeans Cluster method to cluster the distribution areas of the COVID-19 virus. The grouping is done based on the parameters of the number of positive cases and patients who died. This study produced a COVID-19 zoning map. Based on the test, the definite number of clusters is 3 clusters. Where the zoning division based on the COVID-19 Task Force is divided into red for the high zone, orange for the medium zone, yellow for the low zone, and areas that are not exposed to COVID-19 cases are in the green category or there are no cases. The result of this study is a COVID-19 zoning map that is displayed in WEBGIS form.

Application of Fuzzy C Means and TOPSIS in Warehouse Selection at PT Warung Islami Bogor Dewi Primasari; Khidir Zahid Muchtadiabillah; Freza Riana
Jurnal Riset Informatika Vol 5 No 3 (2023): Priode of June 2023
Publisher : Kresnamedia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34288/jri.v5i3.517

PT Warung Islami Bogor needs a warehouse to store goods that come from suppliers. Currently, the selection of warehouses is still done manually and is subjective. It is feared that this will lead to inaccuracies in renting the warehouse. So an application is needed to assist companies in choosing a warehouse. The fuzzy C-Means method can be used to classify warehouse data based on the characteristics of each group. After obtaining the next group is to make a rating of each group. One method that can be used is the TOPSIS method. The TOPSIS method can be applied to this application to rank the data warehouses that have been grouped. In the selection of this warehouse, there are several criteria. The criteria used are price, building area, distance from the head office (HO), parking area, and number of floors. The calculation process is done by dividing the warehouse data into several groups and ranking them to obtain the best recommendations. This application uses the PHP programming language with the Laravel framework—testing using a black box. Fuzzy C-Means and TOPSIS calculations show that Warehouse CCC is the best warehouse in Cluster 1 with a value of 0.797, and the Warehouse in Front of Gas Station Villa Bogor Indah is the best in Cluster 2 with a value of 0.613.

IDENTIFIKASI KUALITAS BENIH JAHE MENGGUNAKAN CONVOLUTIONAL NEURAL NETWORK (CNN) Hendi Hidayat; Freza Riana; Ghibtha Fitri Laxmi; Setyowati Retno
INFOTECH journal Vol. 9 No. 1 (2023)
Publisher : Universitas Majalengka

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31949/infotech.v9i1.5458

Jahe adalah salah satu komoditas ekspor rempah-rempah Indonesia. Peningkatan permintaan jahe belum seimbang dengan produksi jahe. Rendahnya produktivitas jahe ini disebabkan antara lain oleh rendahnya ketersediaan benih jahe yang unggul dan sehat, kurangnya informasi terkait teknik budidaya jahe dan gangguan organisme pengganggu tumbuhan (OPT). Benih pada jahe adalah pembawa sifat genetik pada sebuah tanaman untuk menentukan potensi hasil yang terbaik. Untuk mendapatkan benih yang layak rimpang perlu dilakukan proses pemilahan. Proses pemilahan benih jahe yang berkembang di Indonesia biasanya masih dilakukansecara manual seperti melihat satu per satu benih, hal tersebut merlukan waktu cukup lama. Dari masalah tersebut, maka dibuatlah model algoritma untuk mengidentifikasi kualitas benih jahe melalui sebuah pengolahan citra digital. Convolutional Neural Network (CNN) merupakan metode pengenal pola yang secara langsung mempelajari karakteristik dari benih jahe. CNN dapat membuat model klasifikasi citra benih jahe untuk diukur tingkat akurasi dan peforma model klasifkasi citra digital benih jahe. Pada penelitian ini menggunakan metode CNN yang memiliki tingkat akurasi 88% dalam mengenali citra digital benih jahe dari total dataset sebanyak 1187 citra dengan proporsi data latih 70% (825 citra), data validasi 20% (240 citra), dan data uji 10% (122 citra). Sehingga dapat disimpulkan bahwa model CNN yang dirancang dapat mengidentifikasikan citra dengan baik.

Application of Fuzzy C Means and Topsis in Warehouse Selection at PT. Warung Islami Bogor Dewi Primasari; Khidir Zahid Muchtadiabillah; Freza Riana
Jurnal Riset Informatika Vol. 5 No. 3 (2023): June 2023
Publisher : Kresnamedia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34288/jri.v5i3.223

PT Warung Islami Bogor needs a warehouse to store goods that come from suppliers. Currently, the selection of warehouses is still done manually and is subjective. It is feared that this will lead to inaccuracies in renting the warehouse. So an application is needed to assist companies in choosing a warehouse. The fuzzy C-Means method can be used to classify warehouse data based on the characteristics of each group. After obtaining the next group is to make a rating of each group. One method that can be used is the TOPSIS method. The TOPSIS method can be applied to this application to rank the data warehouses that have been grouped. In the selection of this warehouse, there are several criteria. The criteria used are price, building area, distance from the head office (HO), parking area, and number of floors. The calculation process is done by dividing the warehouse data into several groups and ranking them to obtain the best recommendations. This application uses the PHP programming language with the Laravel framework—testing using a black box. Fuzzy C-Means and TOPSIS calculations show that Warehouse CCC is the best warehouse in Cluster 1 with a value of 0.797, and the Warehouse in Front of Gas Station Villa Bogor Indah is the best in Cluster 2 with a value of 0.613.

IDENTIFIKASI KUALITAS BENIH JAHE MENGGUNAKAN CONVOLUTIONAL NEURAL NETWORK (CNN) Hidayat, Hendi; Riana, Freza; Laxmi, Ghibtha Fitri; Retno, Setyowati
INFOTECH journal Vol. 9 No. 1 (2023)
Publisher : Universitas Majalengka

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31949/infotech.v9i1.5458

Jahe adalah salah satu komoditas ekspor rempah-rempah Indonesia. Peningkatan permintaan jahe belum seimbang dengan produksi jahe. Rendahnya produktivitas jahe ini disebabkan antara lain oleh rendahnya ketersediaan benih jahe yang unggul dan sehat, kurangnya informasi terkait teknik budidaya jahe dan gangguan organisme pengganggu tumbuhan (OPT). Benih pada jahe adalah pembawa sifat genetik pada sebuah tanaman untuk menentukan potensi hasil yang terbaik. Untuk mendapatkan benih yang layak rimpang perlu dilakukan proses pemilahan. Proses pemilahan benih jahe yang berkembang di Indonesia biasanya masih dilakukansecara manual seperti melihat satu per satu benih, hal tersebut merlukan waktu cukup lama. Dari masalah tersebut, maka dibuatlah model algoritma untuk mengidentifikasi kualitas benih jahe melalui sebuah pengolahan citra digital. Convolutional Neural Network (CNN) merupakan metode pengenal pola yang secara langsung mempelajari karakteristik dari benih jahe. CNN dapat membuat model klasifikasi citra benih jahe untuk diukur tingkat akurasi dan peforma model klasifkasi citra digital benih jahe. Pada penelitian ini menggunakan metode CNN yang memiliki tingkat akurasi 88% dalam mengenali citra digital benih jahe dari total dataset sebanyak 1187 citra dengan proporsi data latih 70% (825 citra), data validasi 20% (240 citra), dan data uji 10% (122 citra). Sehingga dapat disimpulkan bahwa model CNN yang dirancang dapat mengidentifikasikan citra dengan baik.

PERBANDINGAN METODE K-NN DAN RANDOM FOREST PADA KLASIFIKASI MAHASISWA BERPOTENSI DROPOUT Rofi, Muhammad Maulana; Setiawan, Foni Agus; Riana, Freza
INFOTECH journal Vol. 10 No. 1 (2024)
Publisher : Universitas Majalengka

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31949/infotech.v10i1.8856

Perguruan tinggi bertanggung jawab memberikan pendidikan terbaik untuk menghasilkan individu berkualitas. Tingginya angka drop out dapat merusak akreditasi. Model dikembangkan menggunakan K-Nearest Neighbor (K-NN) dan Random Forest untuk mengklasifikasikan kasus drop out. Random Forest memiliki akurasi lebih tinggi (99.05%) dibanding K-NN (98.10%). Atribut Persentase Aktif menonjol sebagai faktor paling berpengaruh dalam mengklasifikasikan siswa yang berpotensi putus sekolah, menurut algoritma Random Forest. Ini menandakan pentingnya keterlibatan aktif dalam meminimalkan risiko drop out.

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