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INDONESIA
Jurnal Ilmu Komputer dan Agri-Informatika
Published by Institut Pertanian Bogor
ISSN : 20896026     EISSN : -     DOI : -
Core Subject : Science, Agriculture,
Agriculture, Biological Sciences & Forestry Computer Science & IT
Jurnal Ilmu Komputer dan Agri-Informatika (JIKA) diterbitkan setiap bulan Mei dan November, memuat tulisan ilmiah yang berhubungan dengan bidang Ilmu Komputer serta aplikasi informatika untuk pengembangan pertanian. Berkala ilmiah ini menerima tulisan hasil penelitian dari luar IPB.
Arjuna Subject : -
Articles 187 Documents
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Perbandingan Model AlexNet dan ResNet dalam Klasifikasi Citra Bunga Memanfaatkan Transfer Learning Bana Falakhi; Elmira Faustina Achmal; Muhammad Rizaldi; Renata Rizki Rafi' Athallah; Novanto Yudistira
Jurnal Ilmu Komputer dan Agri-Informatika Vol 9 No 1 (2022)
Publisher : Departemen Ilmu Komputer, Institut Pertanian Bogor

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29244/jika.9.1.70-78

Abstract

Image-based automatic flower species classification is an important issue for biologists creating digital flower catalogs. Many studies on flower species recognition have been proposed so far based on traditional image processing routines. Currently, researchers are applying deep learning to various image-based object recognition tasks. In this paper, deep learning based on transfer learning is applied to the classification of flower species. The proposed methoduses AlexNet and ResNet transfer learning models. The Flower102 dataset which has many categories is used in the experimental work. Various experimental results show that each model has achieved 87% and 96% accuracy performance for AlexNet and ResNet. Theresults obtained show that the effectiveness of the ResNet-based model is higher than the AlexNet-based model.
Pengembangan Sistem Pengukur Curah Hujan di Sungai Jakarta Berbasis IoT Hendra Rahmawan; Dary Muzhar Muhammad; Farianto
Jurnal Ilmu Komputer dan Agri-Informatika Vol 9 No 1 (2022)
Publisher : Departemen Ilmu Komputer, Institut Pertanian Bogor

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29244/jika.9.1.23-36

Abstract

DKI Jakarta is a lowland with high rainfall intensity in certain periods. Currently rainfall is measured manually by officers by visiting directly the Ombrometer rainfall gauge which is placed at a number of points close to several rivers in Jakarta. With such a procedure, the measurement of rainfall becomes inefficient in terms of time and cost because it requires officers who have to visit the measurement location directly and require several additional processes to get the measurement value. This will be difficult if rainfall data is needed in real time, such as for flood early warning purposes. To overcome this problem, a rainfall measuring system is needed that can measure rainfall automatically and send the measurement results in real time via the internet. In this research, an IoT-based rainfall measuring system has been developed using the waterfall method which consists of the stages of defining requirements, designing system, implementing system, and testing system. The system is based on a tipping bucket rainfall sensor whose value can be read and processed directly by the IoT NodeMCU ESP8266 device. The test results have shown that all the functional requirements of the system that have been defined can function properly.
Investigasi Awal Penggunaan Layanan Digital Perguruan Tinggi. Studi Kasus: IPB Mobile for Students Dean Apriana Ramadhan; Firman Ardiansyah; Julio Adisantoso; Auzi Asfarian; Yani Nurhadryani
Jurnal Ilmu Komputer dan Agri-Informatika Vol 9 No 1 (2022)
Publisher : Departemen Ilmu Komputer, Institut Pertanian Bogor

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29244/jika.9.1.37-46

Abstract

Digital services at universities have become necessary, especially after the Covid-19 pandemic disrupted universities. The transformation of the teaching and learning process and its supporting activities from face-to-face to online requires a support system that is inclusive and accessible to the entire higher education community. In 2020, Bogor Agricultural University launched the IPB Mobile for Students application which provides services for students digitally. This research was conducted to conduct an initial investigation of the use of the application. The investigation was carried out using a survey of 198 student respondents from the Bogor Agricultural University in February - March 2022. The questionnaire consisted of two parts: context questions to explore the use of IPB Mobile for Students by students and open questions to seek feedback from students. In general, the results of this initial investigation have insights regarding the use of digital services at IPB, especially in the IPB Mobile for Students application. It can be seen that digital services play an essential role in the teaching and learning process. Student engagement with digital services Reflects a changing pattern or transformation of the standard process for academic services in higher education. This study recommends further studies to examine the use of higher education digital services.
Pembangunan Model Jaringan Saraf Tiruan untuk Memprediksi Kecenderungan Tipe Mediasi Orang Tua terhadap Penggunaan Internet oleh Anak Indah Puspita; Karlisa Priandana; Medria Kusuma Dewi Hardhienata; Peter John Morley; Auzi Asfarian; Husin Alatas
Jurnal Ilmu Komputer dan Agri-Informatika Vol 9 No 1 (2022)
Publisher : Departemen Ilmu Komputer, Institut Pertanian Bogor

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29244/jika.9.1.47-57

Abstract

Mediasi orang tua sangat diperlukan agar dampak negatif penggunaan internet oleh anak yang tinggi di masa pandemi Covid-19 dapat diminimalisir. Penelitian ini dilakukan dengan membuat model jaringan saraf tiruan (JST) untuk mengetahui hubungan antara faktor dalam keluarga dan teknik mediasi orang tua di wilayah Bogor. JST penelitian ini dibangun menggunakan metode pembelajaran propagasi balik (backpropagation). Faktor dalam keluarga yang diteliti sebagai masukan JST adalah usia orang tua, pendidikan, jumlah anak, usia anak, durasi menggunakan internet, serta jumlah media sosial yang digunakan. Jenis mediasi orang tua yang digunakan sebagai luaran jaringan adalah mediasi aktif penggunaan internet umum, mediasi aktif penggunaan bersama, mediasi pasif penggunaan bersama, mediasi pembatasan aktivitas berinternet, mediasi pembatasan penggunaan internet secara umum, mediasi aktif keamanan internet, mediasi pemantauan, dan mediasi teknis penggunaan internet. Data diperoleh melalui survei terhadap 282 orang tua di wilayah Bogor pada Februari-Juni 2021. Penelitian ini telah membangun model JST untuk memprediksi kecenderungan tipe mediasi orang tua dengan mean-squared error sebesar 0.05132. Model yang dihasilkan dapat dikembangkan lebih lanjut menjadi aplikasi edukasi sederhana yang dapat digunakan oleh orang tua untuk mengetahui jenis mediasi yang mereka lakukan. Dengan lebih memahami jenis mediasi yang mereka lakukan, kami berharap orang tua dapat memiliki pemahaman lebih baik mengenai mediasi orang tua dan dapat menerapkan teknik mediasi yang paling sesuai dengan kondisi yang mereka alami untuk mewujudkan ketahanan keluarga.
K-Nearest Neighbor untuk Frasa Guna Mendukung Keputusan dalam Mencari Guru Terbaik Januardi Nasir; Roni Saputra; Gustri Efendi; April Zahmi; Yasha Langitta Setiawan
Jurnal Ilmu Komputer dan Agri-Informatika Vol 9 No 1 (2022)
Publisher : Departemen Ilmu Komputer, Institut Pertanian Bogor

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29244/jika.9.1.13-22

Abstract

There are several ways to assess the best teacher in determining the potential teacher. This aims to encourage teachers to excel and see the motivation, dedication, and loyalty of teachers, as well as see the professionalism of a teacher in technological advancements based on 4.0. From the research conducted in determining the best teacher, there are problems, including in the selection of the best teacher, the principal tends to choose based on observations made only by the principal himself and does not pay attention to the criteria and indicators of assessment in the form of professional, personality and social which makes the teachers less than optimal in their work. Therefore we need a system to solve some of the problems that occur. A system was built to overcome the problem in the form of a decision support system. A decision support system is a system aimed at management in helping to make the right decisions. The method used is K-Nearest Neighbor, which is a method for making decisions using supervised learning where the results of the new input data are classified based on the closest in the value data. Calculations were carried out in 2020 and 2021. The total value of data in the previous year was 70 lines of data. If it is further detailed, the data used is 1190 value data. The assessment prediction data used is teacher data in 2021, as many as 37 lines of data. The k-NN algorithm uses the value of k to determine the number of nearest neighbors whose status will be calculated.
Analisis Sentimen Pengguna Twitter Terhadap Program Vaksinasi Covid-19 di Indonesia Menggunakan Algoritme Support Vector Machine Qarry Atul Chairunnisa; Yeni Herdiyeni; Medria Kusuma Dewi Hardhienata; Julio Adisantoso
Jurnal Ilmu Komputer dan Agri-Informatika Vol 9 No 1 (2022)
Publisher : Departemen Ilmu Komputer, Institut Pertanian Bogor

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29244/jika.9.1.79-89

Abstract

The COVID-19 vaccination policy in Indonesia turns out to be both pros and cons. The government has to evaluate the underlying reason of why some people are against the policy, so that the vaccination program can run smoothly. Sentiment analysis as a way to see the polarity of opinion, makes it possible to classify positive, negative or neutral responses on Twitter regarding the vaccination policy. This study aims to determine the public's response to COVID-19 vaccination in Indonesia by examining word distribution and creating a Support Vector Machine (SVM) classification model. Sentiment analysis consists of several stages, namely data collection, data preprocessing, data weighting, data analysis, data sharing, classification modeling, hyperparameter tuning and model evaluation. The results of this study are a model with a relatively optimal performance in classifying sentiment with an accuracy, precision, recall and f1-score of 90%. The results of the sentiment analysis obtained are in the form of ideas, complaints, and suggestions for the COVID-19 vaccination.
Prediksi Kandungan Lignin pada Dedak Padi Bercampur Sekam Menggunakan Tekstur Statistik dan KNN Eylen Desy Novita; Aziz Kustiyo; Anuraga Jayanegara; Toto Haryanto; Hari Agung Adrianto
Jurnal Ilmu Komputer dan Agri-Informatika Vol 9 No 1 (2022)
Publisher : Departemen Ilmu Komputer, Institut Pertanian Bogor

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29244/jika.9.1.58-69

Abstract

Adulteration in rice bran happens quite high due to the expensive price of rice bran. Mixing the rice bran with husk could decrease the rice bran quality because the content of crude fiber and lignin cointained in husk are anti-nutrients. Lignin content can be estimated by the texture of rice bran mixed with husk image. This study aimed to analyze the texture of rice bran mixed with husk image using run length feature extraction method with k-nearest neighbour (KNN) classification. The images of rice bran mixed with husk were taken using Dino Capture digital microscope with magnification 200 times. The images were generated with the spatial resolution of 640×480 pixels in a bitmap format. Those images were converted from RGB into grayscale in preprocessing phase, then the result of grayscale images were enhanced using histogram equalization as image enhancement method. The training and testing was determined using 5-fold cross validation with 3 repetition. The result of KNN classification with 7 features showed the highest accuracy of 74.55%.
Pemodelan Berbasis Jaringan untuk Pengklasifikasian Kanker Payudara Berdasarkan Data Molekuler Mushthofa; Chamdan L Abdulbaaqiy; Sony Hartono Wijaya; Muhammad Asyhar Agmalaro; Lailan Sahrina Hasibuan
Jurnal Ilmu Komputer dan Agri-Informatika Vol 9 No 1 (2022)
Publisher : Departemen Ilmu Komputer, Institut Pertanian Bogor

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29244/jika.9.1.101-113

Abstract

Cancer is a disease characterized by uncontrolled cell growth. One of the characteristics of uncontrolled growth is the presence of estrogen-receptor-positive (ER+). About 67% of breast cancer test results have ER+. Breast cancer profiles are divided into 4 subtypes, namely: Luminal A, Luminal B, basal-like, and HER-2 enriched. Each category has a different effect on adjuvant chemotherapy. In this study, a network-based approach was used to select features/molecular biomarkers that have the potential to assist modeling and classifying sub-types of breast cancer. The molecular features used are Copy Number Alteration (CNA) and gene expression. The feature selection results were compared with the PAM50 feature-based accuracy from the literature study. The results indicate that the features selected from this network-based approach can obtain a comparable performance w.r.t the original PAM50 features, and can be used as alternative to perform breast cancer subtyping.
Penerbangan Otomatis Pesawat Tanpa Awak Sayap Tetap Menggunakan Flight Controller Berbasis iNav Auriza Rahmad Akbar; Ali Imron
Jurnal Ilmu Komputer dan Agri-Informatika Vol 9 No 1 (2022)
Publisher : Departemen Ilmu Komputer, Institut Pertanian Bogor

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29244/jika.9.1.90-100

Abstract

Pesawat tanpa awak (UAV) dapat dikendalikan secara manual dengan remote control atau secara otomatis dengan flight controller (FC). Sangat sedikit penelitian yang membahas konfigurasi penerbangan otomatis UAV sayap tetap dengan memakai firmware iNav. Oleh karena itu, penelitian ini bertujuan untuk melakukan penerbangan otomatis pada UAV jenis sayap tetap dengan iNav. UAV yang digunakan adalah model SkySurfer X8 dengan bentang sayap 1.40 m. FC yang digunakan adalah Matek F405-Wing. Metode penelitian ini terdiri atas lima tahap: perakitan, konfigurasi, pengujian, pengambilan dan pemrosesan data, dan analisis data. Berdasarkan hasil uji terbang, UAV berhasil terbang secara otomatis mengikuti skenario yang diberikan. Skenario terpanjang berupa persegi berukuran 600×600 m pada ketinggian 100 m, yang ditempuh dalam waktu sekitar 2 menit dengan kecepatan sekitar 65 km/jam. Hasil dari penelitian ini berupa prosedur perakitan, konfigurasi, operasi, dan hasil data uji terbang diharapkan dapat menjadi acuan dalam penelitian UAV sayap tetap menggunakan iNav.
Penerapan Konsep Internet of Things Pada Sistem Monitoring Volume Timbulan Sampah di Tempat Penampungan Sementara Kota Bogor Sri Wahjuni; Wulandari; Rizqi Alifahasni Zakiah
Jurnal Ilmu Komputer dan Agri-Informatika Vol 9 No 2 (2022)
Publisher : Departemen Ilmu Komputer, Institut Pertanian Bogor

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29244/jika.9.2.114-126

Abstract

Sampah masih menjadi salah satu masalah yang sulit untuk ditangani oleh pemerintah Kota Bogor. Dengan jumlah penduduk mencapai angka 1.08 juta jiwa, jumlah sampah yang dihasilkan perharinya mencapai 2 532.5 liter. Jumlah sampah yang tergolong banyak ini hanya diakomodasi oleh jumlah armada angkutan sampah yang terbatas. Hal ini mengakibatkan sampah di Tempat Penampungan Sementara (TPS) menumpuk sehingga mencemari lingkungan. Oleh karena itu, diperlukan sebuah sistem monitoring jarak jauh untuk mengetahui volume timbulan sampah. Penelitian ini membangun sebuah prototype untuk mengetahui volume timbulan sampah secara real-time dan memberikan notifikasi untuk pengambilan sampah di luar jadwal regular, yaitu ketika volume timbulan sampah mendekati volume maksimal bak sampah. Metode yang digunakan dalam penelitian adalah frame difference algorithm dan perspective transformation. Selain itu, penelitian ini juga menerapkan konsep Internet of Things dalam aplikasi berbasis mobile sebagai sistem monitoring dan notifikasi. Nilai error atau RMSE antara tinggi aktual volume timbulan sampah dan tinggi timbulan volume sampah hasil pengolahan citra yang didapat adalah 0.2060 dan akurasi sebesar 97.93 persen.

Page 12 of 19 | Total Record : 187

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