Garuda - Garba Rujukan Digital

p-Index From 2019 - 2024

4.427

P-Index

This Author published in this journals

All Journal IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Epsilon: Jurnal Matematika Murni dan Terapan Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) Telematika KLIK (Kumpulan jurnaL Ilmu Komputer) (e-Journal) Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Jurnal Komputasi Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Journal of Electronics, Electromedical Engineering, and Medical Informatics Indonesian Journal of Electrical Engineering and Computer Science JUSTIN (Jurnal Sistem dan Teknologi Informasi) Jurnal Pengabdian Ilung (Inovasi Lahan Basah Unggul) Journal of Data Science and Software Engineering Jurnal Teknik Ilmu dan Aplikasi Jurnal Informatika Polinema (JIP)

Saragih, Triando Hamonangan

Universitas Lambung Mangkurat

Author-ID : 2989781

Agriculture, Biological Sciences & Forestry Arts Humanities Astronomy Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Mechanical Engineering Medicine & Pharmacology Nursing Physics Public Health Social Sciences Transportation Other

Published : 34 Documents Claim Missing Document

Claim Missing Document

Articles

1 2 3 4

Klasifikasi Tanaman Jarak Pagar Menggunakan Algoritme Deep Learning H2O Mazdadi, Muhammad Itqan; Ramadhani, Rahmat; Saragih, Triando Hamonangan; Haekal, Muhammad
Jurnal Komputasi Vol 9, No 1 (2021): Komputasi
Publisher : Universitas Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/komputasi.v9i1.2774

Tanaman jarak pagar merupakan tanaman multi fungsi yang memiliki banyak manfaat dari daun hingga buah. Tanaman jarak pagar sering digunakan untuk produk kecantikan hingga pengganti biodiesel. Penyakit yang menyerang tanaman jarak pagar dapat mengganggu hasil dari tanaman jarak pagar. Kurangnya pakar dibidang ini dan pengetahuan yang dimiliki petani menyebabkan sesuatu yang buruk. Persoalan ini dapat diselesaikan dengan metode Deep Learning. Metode Deep Learning yang digunakan adalah H2O. H2O digunakan karena dapat memberikan hasil komputasi yang cepat dan bisa memberikan akurasi yang baik. Pada penelitian ini bisa kita lihat bahwa H2O memberikan akurasi rata-rata maksimal sebesar 96,066% dengan parameter uji kombinasi data latih dan data uji 60:40, menggunakan satu layer dan jumlah epoch sebanyak 100. Pada penelitian ini membuktikan bahwa H2O bisa digunakan untuk identifikasi penyakit tanaman jarak pagar.

AdaBoost Classifier untuk Klasifikasi Tanaman Jarak Pagar Triando Hamonangan Saragih; Muliadi Muliadi; Mohammad Reza Faisal; Muhammad Al Ichsan Nur Rizqi Said
Jurnal Komputasi Vol 9, No 2 (2021)
Publisher : Universitas Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/komputasi.v9i2.2865

Tanaman Jarak Pagar merupakan tanaman multi fungsi yang memiliki banyak kegunaan di kehidupan sehari-hari, baik itu untuk pengobatan, kecantikan hingga pengganti bahan bakar biodiesel. Penyakit yang menyerang tanaman jarak pagar dapat menurunkan kualitas yang dihasilkan jarak pagar. Minimnya pengetahuan petani dan sedikitnya jumlah pakar yang memahami tentang jarak pagar menjadi masalah yang harus diselesaikan. Pengguanaan sistem pakar menjadi solusi yang bisa ditawarkan. AdaBoost Classifier pada sistem pakar dapat digunakan sebagai mengklasifikasikan penyakit tanaman jarak pagar. Hasil yang diperoleh dari penelitian ini yaitu didapat akurasi rata-rata sebesar 50% dan maksimal terbaik sebesar 53,01% pada jumlah fold sebanyak 2. Hasil pada penelitian ini lebih baik dibanding penelitian sebelumnya, tetapi tidak bisa memberikan hasil yang maksimal. Jumlah data tiap kelas menjadi perrmalasahan mengapa hasil pada AdaBoost kurang maksimal dan harus diselesaikan pada penelitian selanjutnya.

Jatropha Curcas Disease Identification using Random Forest Triando Hamonangan Saragih; Vivi Nur Wijayaningrum; Muhammad Haekal
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol 7, No 1 (2021): April
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v7i1.20141

As one of the most versatile plants, Jatropha curcas is spread in various regions around the world because of the great benefits it provides. However, Jatropha curcas is easily attacked by viruses which then cause damage to the plant, such as yellowing leaves and secreting sap, making it necessary to identify Jatropha curcas disease to deal with the problem as early as possible so that the losses incurred are not too large. An expert system was built to be able to identify Jatropha curcas disease by adopting expert knowledge. The use of the Random Forest algorithm as one of the classification algorithms was applied in this study. By using a random forest, several disease prediction classes are generated by each decision tree that has been formed. The disease class with the most votes was used as the final result. In this study, the data used were 166 data with 9 diseases and 30 symptoms. The results showed that Random Forest outperformed other algorithms such as Fuzzy Neural Network and Extreme Learning Machine with an accuracy of 98.002% using the composition of training data and test data of 70:30.

EFESIENSI ENERGI PADA BANGUNAN MENGGUNAKAN MULTIVARIATE RANDOM FOREST Triando Hamonangan Saragih; Mohammad Reza Faisal; Muhammad Haekal
KLIK- KUMPULAN JURNAL ILMU KOMPUTER Vol 9, No 1 (2022)
Publisher : Lambung Mangkurat University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20527/klik.v9i1.421

Energy is needed by humans. Energy utilization is often carried out in daily activities, such as helping with work, household activities to lighting both at home and on the road. Recently, there has been a lot of research on concerns about the waste of energy and its lasting adverse impact on the environment. Previous research conducted by Tsanas and Xifara in 2012 has carried out energy efficiency in buildings using Statistical Machine Learning. Their research focuses on calculating outcomes one by one, not directly on all outcomes. In this study using the Multivariate Random Forest method. Multivariate Random Forest has similarities compared to Random Forest, while the Multivariate Random Forest method is more used if more than one output is produced. Based on the tests that have been carried out, it can be concluded that the best parameter that gives maximum results is the number of trees as many as 200 with a data division of 60% training data and 40% testing data with RMSE results of 2.602036 and MSE result of 6.770589. Based on the tests that have been carried out, it proves that the more the number of trees does not prove that it can provide maximum results.Keywords: Energy, Efficiency, Prediction, Multivariate Random ForestEnergi sangat dibutuhkan oleh manusia. Pemanfaatan energi sering dilakukan dalam kegiatan sehari-hari, seperti membantu pekerjaan, kegiatan rumah tangga hingga penerangan baik dalam rumah maupun di jalan. Akhir-akhir ini banyak penelitian tentang kekhawatiran mengenai pemborosan energi dan dampak buruknya yang abadi terhadap lingkungan. Penelitian sebelumnya yang dilakukan oleh Tsanas dan Xifara pada tahun 2012 telah melakukan efesiensi energi pada bangunan menggunakan Statistical Machine Learning. Penelitian mereka berfokus pada perhitungan luaran secara satu persatu, tidak secara langsung semua luaran. Pada penelitian ini menggunakan metode Multivariate Random Forest. Multivariate Random Forest memiliki kesamaan dibandingkan dengan Random Forest, sedangkan metode Multivariate Random Forest lebih digunakan jika luaran yang dihasilkan lebih dari satu. Berdasarkan pengujian yang sudah dilakukan, dapat disimpulkan bahwa parameter terbaik yang memberikan hasil maksimal yaitu pada jumlah pohon sebanyak 200 dengan pembagian data sebanyak 60% data latih dan 40% data uji dengan hasil RMSE sebesar 2.602036 dan MSE sebesar 6.770589. Berdasarkan pengujian yang sudah dilakukan membuktikan semakin banyak jumlah pohon tidak membuktikan bisa memberikan hasil yang maksimal.Kata kunci: Energi, Efesiensi, Prediksi, Multivariate Random Forest

OPTIMASI FUNGSI KEANGGOTAAN FIS TSUKAMOTO MENGGUNAKAN SIMULATED ANNEALING UNTUK IDENTIFIKASI PENYAKIT GIGI Triando Hamonangan Saragih; Rahmat Ramadhani; Muhammad Itqan Mazdadi; Ahmad Rusadi Arrahimi; Mohammad Reza Faisal
KLIK- KUMPULAN JURNAL ILMU KOMPUTER Vol 7, No 3 (2020)
Publisher : Lambung Mangkurat University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20527/klik.v7i3.349

Teeth are one of the tools in the framework related to the human stomach which fills as a food destroyer for simple processing. Diseases that attack teeth can withstand this action and cannot be distinguished quickly by young dental specialists. This problem can be solved by methods in the field of technology. The algorithm that can be used is FIS Tsukamoto in classification. Optimization of the membership function at FIS Tsukamoto is needed to improve accuracy. Optimization of FIS Tsukamoto membership function using Simulated Annealing produced the highest accuracy at 92.5% of the 100 test data.Keywords: Simulated Annealing; FIS Tsukamoto, Dental Disease, Optimization Gigi adalah salah satu alat dalam kerangka terkait perut manusia yang mengisi sebagai penghancur makanan untuk pemrosesan sederhana. Penyakit yang menyerang gigi dapat menahan tindakan ini dan tidak dapat dibedakan dengan cepat oleh dokter muda spesialis gigi. Masalah ini dapat diselesaikan dengan metode di bidang teknologi. Algoritma yang bisa digunakan yaitu FIS Tsukamoto dalam melakukan klasifikasi. Optimasi fungsi keanggotaan pada FIS Tsukamoto diperlukan untuk meningkatkan akurasi. Optimasi fungsi keanggotaan FIS Tsukamoto menggunakan Simulated Annealing menghasilkan akurasi paling tinggi yaitu 92,5% dari 100 data uji.Kata kunci: Simulated Annealing; FIS Tsukamoto, Penyakit Gigi, Optimisasi

Prediksi Tinggi Permukaan Air Waduk Menggunakan Artificial Neural Network Berbasis Sliding Window Dwi Kartini; Friska Abadi; Triando Hamonangan Saragih
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 5 No 1 (2021): Februari 2021
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

The water level in the reservoir is an important factor in the operation of a hydroelectric turbine to control water overflow so that there is no excessive degradation. This water control has an influence on the performance and production of hydroelectric energy. The daily reservoir water level (tpaw) recording of PLTA Riam Kanan is carried out through a daily direct measurement and observation process on the reservoir measuring board which is recapitulated every month in excel form. This time series historical data continues to grow every day to become a data warehouse that is still useless if only stored. Extracting knowledge from the data warehouse can be done using one of the artificial neural network data mining techniques, namely backpropagation to predict the next day's tpaw. Historical data for the tpaw time series is presented with a sliding window concept approach based on the window sizes used, namely 7, 14, 21 and 28. Some backpropagation network testing is carried out using a combination of the number of window sizes against the comparison of the amount of training data and test data on the network. The prediction results obtained with the smallest mean squared error (mse) in network testing is 0.000577 as a high accuracy value of the prediction results. The network architecture with the smallest mse using 28 input layers, 10 hidden layers and 1 output layer can be a knowledge that can help the hydropower plant as an alternative in making turbine operation decisions based on the predicted results of reservoir water level.

Comparative Study of Decision Tree, K-Nearest Neighbor, and Modified K-Nearest Neighbor on Jatropha Curcas Plant Disease Identification Triando Hamonangan Saragih; Diny Melsye Nurul Fajri; Alfita Rakhmandasari
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Vol. 5, No. 1, February 2020
Publisher : Universitas Muhammadiyah Malang

Jatropha Curcas is a very useful plant that can be used as a bio fuel for diesel engines replacing the coal. In Indonesia, there are few plantation that plant Jatropha Curcas. But there is so limited farmers that understand in detail about the disease of Jatropha Curcas and it may cause a big loss during harvesting when the disease occured with no further action. An expert system can help the farmers to identify the lant diseases of Jatropha Curcas. The objective of this research is to compare several identification and classification methods, such as Decision Tree, K-Nearest Neighbor and its modification. The comparison is based on the accuracy. Modified K-Nearest Neighbor method given the best accuracy result that is 67.74%.

Penerapan Kolam Terpal Bioflok Ikan Lele Tenaga Surya bagi Warga Aliran Anak Sungai Kemuning di Kelurahan Loktabat Utara Dodon Turianto Nugrahadi; Muhammad Itqan Mazdadi; Triando Hamonangan Saragih; Totok Wianto
Jurnal Pengabdian ILUNG (Inovasi Lahan Basah Unggul) Vol 1, No 1 (2021)
Publisher : Universitas Lambung Mangkurat

Dodon Turianto Nugrahadi*1, Muhammad Itqan Mazdadi 2, Triando Hamonangan S3, Totok Wianto41,2,3,4 Universitas Lambung Mangkurat1,2,3Program Studi Ilmu Komputer, FMIPA, Universitas Lambung Mangkurat4Program Studi Fisika, FMIPA, Universitas Lambung Mangkurat*e-mail: dodonturianto@ulm.ac.id1, mazdadi@ulm.ac.id2,triando.saragih@ulm.ac.id3, totokwianto@ulm.ac.id4Received: 25 Mei 2021/ Accepted: 16 Juni 2021 AbstractPeople on the side river of the Kemuning river in the North Loktabat sub-district have not utilized the river water as a source of fisheries business. With river water sources for use as fisheries, it can provide alternative livelihoods for people on the side river of the Kemuning river. However, if they use fishery media such as keramba, the water source of the Kemuning river will overflow in the rainy season, besides that if you use a pond, you need a land medium that is less possible.The use of a kolam terpal is one solution to this problem. This pool is made based on the need for a portable pool because it has a radius of 1.5m and a height of 1.5m. Kolam terpal are fish farming using tarpaulin materials as an alternative to soil or concrete ponds. The pool with the base and the sides of the walls is made of tarpaulin. The tarpaulin needed to make this pool is a type of tarpaulin with a material that is pressed so that no leakage occurs. The implementation of this tarpaulin pool using biofloc techniques and by using solar power makes this pool easier to adapt. This tool is equipped with solar panels and an automatic control system. The use of solar panels by utilizing the abundance of solar power and minimizing the expenses of fish farmers without electricity bills to activate pumps for water needs and pond air aerators. In addition, with the biofloc technique, the fish farming mechanism becomes more efficient.The targets and outputs generated from this program, especially for partners, are: the fulfillment of alternative livelihoods with fish farming, and an increase in income of approximately 80%, there is a biofloc kolam terpal equipment with solar panel technology. Keywords: Tarpaulin pool, solar power, bioflok AbstrakWarga di pesisir aliran anak sungai kemuning daerah Kelurahan Loktabat Utara belum memanfaatkan aliran anak sungai sebagai sumber usaha perikanan. Dengan sumber air sungai untuk pemanfaatan sebagai usaha perikanan dapat memberikan alternatif mata pencaharian bagi warga di pesisir aliran anak sungai kemuning. Akan tetapi jika menggunakan media perikanan seperti keramba, sumber air anak sungai kemuning dapat terjadi luapan jika dimusim hujan, selain itu jika menggunakan kolam tambak membutuhkan media lahan yang kurang memungkinkan. Penggunaan kolam terpal menjadi salah satu solusi untuk mengatasi masalah tersebut, kolam ini dibuat berdasarkan kebutuhan akan kolam yang portabel karena ukuranya jari-jari 1,5m dan tinggi 1,5m. Kolam terpal merupakan budidaya ikan dengan menggunakan bahan terpal sebagai alternative kolam tanah atau beton. Kolam yang dasarnya maupun sisi-sisi dindingnya dibuat dari terpal. Terpal yang dibutuhkan untuk membuat kolam ini adalah jenis terpal dengan bahan dipres sehingga tidak terjadi kebocoran. Implementasi kolam terpal ini dengan menggunakan teknik bioflok serta dengan menggunakan tenaga surya maka kolam ini dapat lebih mudah diadaptasikan. Alat ini dilengkapi dengan panel surya dan sistem kontrol otomatis. Penggunaan panel surya dengan memanfaatkan limpahan tenaga surya dan meminimalkan pengeluaran para pembudidaya ikan tanpa adanya tagihan listrik untuk mengaktifkan pompa untuk kebutuhan air dan aerator udara kolam. Selain itu dengan teknik bioflok, mekanisme pembudidayaan ikan menjadi lebih efisien.Target dan luaran yang dihasilkan dari program ini khususnya untuk pihak mitra adalah: terpenuhinya alternatif mata pencaharian dengan budidaya ikan, dan meningkatkan pendapat kurang lebih 80%, terdapat peralatan kolam terpal bioflok dengan teknologi panel surya. Kata kunci: Kolam terpal, tenaga surya, bioflok

Optimization of Dempster-Shafer’s Believe Value Using Genetic Algorithm for Identification of Plant Diseases Jatropha Curcas Triando Hamonangan Saragih; Wayan Firdaus Mahmudy; Yusuf Priyo Anggodo
Indonesian Journal of Electrical Engineering and Computer Science Vol 12, No 1: October 2018
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v12.i1.pp61-68

Jatropha curcas is a plant that can be used as a substitute for diesel fuel. Lack of knowledge of farmers and the limited number of experts and extension agents into the problem of dealing with the disease Jatropha curcas plant which resulted in lower quality of Jatropha curcas. Dempster-Shafer method can be a solution for decision making based on previous research. The difference in beliefs of every expert in seeing Jatropha diseases are important because Dempster-Shafer can not solve this problem. Optimization using genetic algorithms can solve this problem. Optimization of belief values using genetic algorithms can improve the accuracy of the results of this system are using Dempster-Shafer. On the results of this system provides the highest system accuracy value, opimization of belief values using genetic algorithms gives a more significant result than the use of Dempster-Shafer only.

Jatropha Curcas Disease Identification With Extreme Learning Machine Triando Hamonangan Saragih; Diny Melsye Nurul Fajri; Wayan Firdaus Mahmudy; Abdul Latief Abadi; Yusuf Priyo Anggodo
Indonesian Journal of Electrical Engineering and Computer Science Vol 12, No 2: November 2018
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v12.i2.pp883-888

Jatropha is a plant that has many functions, but this plant can be attacked by various diseases. Expert systems can be applied in identifying so that can help both farmers and extension workers to identify the disease. one of method that can be used is Extreme Learning Machine. Extreme Learning Machine is a method of learning in Neural Network which has a one-time iteration concept in each process. In this study get a maximum accuracy of 66.67% with an average accuracy of 60.61%. This proves the identification using Extreme Learning Machine is better than the comparison method that has been done before.

Co-Authors Abdul Latief Abadi Ahmad Rusadi Arrahimi - Universitas Lambung Mangkurat) Ahmad Rusadi Arrahimi - Universitas Lambung Mangkurat) Ajwa Helisa Alfita Rakhmandasari Amelia Aditya Santika Andi Farmadi Andi Farmadi Dina Arifah Diny Melsye Nurul Fajri Diny Melsye Nurul Fajri Dodon Turianto Nugrahadi Dwi Kartini Dwi Kartini, Dwi Fatma Indriani Fatma Indriani Friska Abadi Haekal, Muhammad Irwan Budiman Ivan Sitohang MARIA ULFAH Mariana Dewi Muhamad Fawwaz Akbar Muhammad Al Ichsan Nur Rizqi Said Muhammad Darmadi Muhammad Fauzan Nafiz Muhammad Haekal Muhammad Haekal Muhammad Itqan Mazdadi Muhammad Meftah Mafazy Muhammad Reza Faisal, Muhammad Reza Muhammad Ridho Ansyari Muhammad Rofiq Muliadi Muliadi Muliadi Muliadi Muliadi Muliadi Muliadi Muliadi Nurlatifah Amini Purwoko, Agus Radityo Adi Nugroho Rahmat Ramadhani Rahmat Ramadhani Ramadhani, Rahmat Ratna Septia Devi Regina Rudy Herteno Rudy Herteno SALLY LUTFIANI Salsha Farahdiba Siti Napi'ah Suci Permata Sari Sulastri Norindah Sari Totok Wianto Vivi Nur Wijayaningrum Wayan Firdaus Mahmudy Yanche Kurniawan Mangalik Yusuf Priyo Anggodo

Title

Found 34 Documents
Search

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Title Search

Found 34 Documents Search

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Title

Found 34 Documents
Search