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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Jurnal Edukasi dan Penelitian Informatika (JEPIN) JUITA : Jurnal Informatika Majalah Ilmiah MOMENTUM JOIN (Jurnal Online Informatika) Jurnal Teknologi dan Sistem Komputer JOIV : International Journal on Informatics Visualization Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Electro Luceat Jurnal Informatika Universitas Pamulang Applied Information System and Management Jurnal Teknoinfo J-SAKTI (Jurnal Sains Komputer dan Informatika) Jurnal Tekno Kompak Building of Informatics, Technology and Science INTI Nusa Mandiri JTIKOM: Jurnal Teknik dan Sistem Komputer Jurnal Teknologi dan Sistem Tertanam Journal Social Science And Technology For Community Service J-SAKTI (Jurnal Sains Komputer dan Informatika) Jurnal Telematics and Information Technology (TELEFORTECH) Journal of Engineering and Information Technology for Community Service Jurnal Ilmiah Edutic : Pendidikan dan Informatika Jurnal Informatika: Jurnal Pengembangan IT Jurnal Media Celebes Jurnal Media Jawadwipa

Andi Nurkholis

Universitas Teknokrat Indonesia

Author-ID : 2490008

Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Languange, Linguistic, Communication & Media Mathematics Social Sciences Other

Published : 57 Documents Claim Missing Document

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Articles

Title

Algoritme Spatial Decision Tree untuk Evaluasi Kesesuaian Lahan Padi Sawah Irigasi Andi Nurkholis; Muhaqiqin; Try Susanto
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 4 No 5 (2020): Oktober 2020
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Agriculture has a strategic role in a country whereas food self-sufficiency being the main goal to be achieved. Indonesia has set a strategic plan for increasing the productivity of several commodities, including rice, especially irrigated lowland rice. That matter can be done by agricultural land extensification, which requires a land suitability directional map. This study aims to produce irrigated lowland rice land suitability maps which can be obtained by evaluation using spatial decision tree algorithm. The model is made in two different types, where model Y is an optimized version of model X. The dataset consists of two categories, namely eleven explanatory layers which are land and weather characteristics, and a target layer that represents irrigated lowland rice land suitability in study area of Grobogan Regency, Central Java Province. As an addition to planting requirements, two spatial weather datasets were generated using ordinary cokriging interpolation, which was not used in previous research, while actually being important element for determining plant timing an agricultural commodity. Based on accuracy, model Y is the best model with 96.67%, compared to model X with 86%. Both models make relief variable as the root node, but in spatial decision tree result, model X involves all variables, while model Y does not involve an elevation variable. The addition of weather variables in models is appropriate, as evidenced by the involvement in rules.

Optimasi Parameter Support Vector Machine Berbasis Algoritma Firefly Pada Data Opini Film Styawati; Andi Nurkholis; Zaenal Abidin; Heni Sulistiani
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 5 No 5 (2021): Oktober2021
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

The Support Vector Machine (SVM) method is a method that is widely used in the classification process. The success of the classification of the SVM method depends on the soft margin coefficient C, as well as the parameter  of the kernel function. The SVM parameters are usually obtained by trial and error, but this method takes a long time because they have to try every combination of SVM parameters, therefore the purpose of this study is to find the optimal SVM parameter value based on accuracy. This study uses the Firefly Algorithm (FA) as a method for optimizing SVM parameters. The data set used in this study is data on public opinion on several films. Class labels used in data classification are positive class labels and negative class labels. The amount of data used in this study is 2179 data, with the distribution of 436 data as test data and 1743 data as training data. Based on this data, an evaluation process was carried out on the Firefly Algorithm-Support Vector Machine (FA-SVM). The results of this study indicate that the Firefly Algorithm can obtain the optimal combination of SVM parameters based on accuracy, so there is no need for trial and error to get that value. This is evidenced by the results of the FA-SVM evaluation using a value range of C=1.0-3.0 and =0.1-1.0 resulting in the highest accuracy of 87.84%. The next evaluation using a range of values C=1.0-3.0 and =1.0-2.0 resulted in the highest accuracy of 87.15%.

Comparison of Kernel Support Vector Machine Multi-Class in PPKM Sentiment Analysis on Twitter Andi Nurkholis; Debby Alita; Aris Munandar
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 6 No 2 (2022): April 2022
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

PPKM is the Indonesian government's policy to deal with the spread of the coronavirus since early 2021. Until now, PPKM is still the main topic to prevent the spread of COVID-19. This policy has generated various responses from the public, especially on Twitter. A sentiment analysis process is needed to process the text obtained from Twitter. Sentiment analysis is a form of representation of text mining and text processing. This study aims to analyze public sentiment towards PPKM through data obtained from Twitter using the multi-class SVM algorithm. In implementing multi-class SVM, an analysis of the Polynomial and RBF kernels was carried out on the One Against One and One Against Rest methods which showed that the combination of One Against Rest and the Polynomial kernel was obtained the best accuracy, which was 98.9%. Unlike the case with the combination of One Against One and Kernel RBF, which obtained the worst accuracy, 77.6%. The best model produces precision, recall, and f1-score values of 97%, 98%, and 97%. Based on the confusion matrix results, the best model has a positive class distribution = 912, neutral = 51, and negative = 26. Overall, the polynomial kernel model produces higher accuracy; both applied to the One Against One and One Against Rest methods. In contrast, the RBF kernel model produces lower accuracy and is significantly different when applied to the One Against One and One Against Rest methods. The model results show that public sentiment towards the PPKM policy is positive to be continued consistently to suppress the spread of the COVID-19 virus.

Co-Authors Adhie Thyo Priandika Adi Sucipto, Adi Ady Candra Nugroho Afifudin Afifudin Aftirah, Nadia Agung Riyantomo Ahmad Ari Aldino Aldi Bagus Prasetyo Alita, Debby Alvi Suhartanto Andrey Ferriyan, Andrey Anjumi, Krisma Nur Annisa Annisa Ans, Faris Arkan Arfat, Muhammad Fadilah Arief Budiman Aris Munandar Bagas Aditama Bagus Miftaq Hurohman Berlintina Permatasari Dalimunthe, Ernando Rizki Damayanti, Damayanti Donaya Pasha Dyah Ayu Megawaty Eka Saputra Ellin Gusbriana Erliyan Redy Susanto Fahreza Aditya Aryatama Faris Arkans Ans Fernando, Yusra Firmansyah, Ilham Gusti Firmansyah Gustian Rama Putra Harry Gunawan Heni Sulistiani I Ketut Wahyu Gunawan Imas Sukaesih Sitanggang Indra Kurniawan Indra Kurniawan Irsan, Aqilla Hattami Irwan Tubagus Isnain, Auliya Rahman Iwan Syahputra johansyah johansyah Johansyah Johansyah Jupriyadi Jupriyadi Jupriyadi, Jupriyadi Kartini, Nuri Koeswara, Wawan Leny Meilisa M Fabian Apriando Maria Ainun Nazar Mega Desi Diah Ayu Megawaty, Dyah Ayu Mohammad Tafrikan Muhammad Aldhi Septianto Muhammad Fadilah Arfat Muhammad Fauzan Ramadhani Muhammad Fitratullah Muhammad Hamdan Sobirin Muhaqiqin Muhaqiqin muhaqiqin Nadia Aftirah Nadiya Safitri Neneng Neneng Ni’mawati, Akfina Oktora, Putri Suci Pasaribu, A. Ferico Octaviansyah Pasha, Donaya Prasetyo, Aditya Dwi Pria Agung Laksono Purwayoga, Vega Rafi Athallah Rahayu, Masnia Rahayu, Ririn Wuri Ramadhani, Muhammad Fauzan Renda Bimantara Rikendry Rikendry Rio Andika Rulyansyah Permata Putra S. Samsugi Sakti, Hakim Erlangga Bernado Sampurna Dadi Riskiono Saputra, Alvin Saputra, Hendi Setiawansyah Setiawansyah Sitanggang, Imas S. Siti Yuliyanti, Siti Sobir Sobir Sokid, Sokid Styawati Styawati Styawati, S Styawati, Styawati Suhartanto, Alvi Susanto, Erliyan Redy Syahirul Alim Syahirul Alim Syaiful Ahdan Temi Ardiansah Tia Nanda Pratiwi Tiara Azizul Andika Tiyas Utami Tri Widodo Try Susanto Veithzal Rivai Zainal Wahyu Sardjono Wawan Koswara Wijaya, Suhenda Yeris Ari Sandi Yopita Anggela Yuri Rahmanto Yusra Fernando Zaenal Abidin Zahra Kharisma Sangha Zahrina Amalia Zainabun Mardiyansyah

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