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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 Jurnal Padamu Negeri
Andi Nurkholis
Universitas Teknokrat Indonesia
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

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APLIKASI AUGMENTED REALITY MENGGUNAKAN METODE MARKERLESS UNTUK PROMOSI UNDANGAN BERBASIS MOBILE Nurkholis, Andi; Megawaty, Dyah Ayu; Irsan, Aqilla Hattami
Jurnal Teknoinfo Vol 19, No 1 (2025): January 2025
Publisher : Universitas Teknokrat Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33365/jti.v19i1.4597

Abstract

Pelayanan dan pengelolaan informasi pada percetakan CV. Jastra card sejauh ini masih menggunakan cara konvensional untuk mempromosikan percetakan undangan pelanggan datang ke percetakan untuk melihat undangan yang tersedia. Promosi konvensional tersebut menghadapi beberapa tantangan, seperti halnya biaya yang tinggi, terbatasnya jangkauan, serta perkembangan tren digital. Dengan semakin berkembangnya teknologi dan penetrasi internet, banyak orang beralih ke media digital untuk mencari informasi dan produk. Promosi konvensional kurang efektif dalam menjangkau target pasar yang lebih muda atau lebih terhubung secara digital. Untuk mengatasi permasalahan ini, bisnis undangan dapat mempertimbangkan strategi promosi yang lebih terarah dan efektif. Beberapa contoh strategi yang bisa diterapkan adalah memanfaatkan pemasaran digital, beriklan secara online melalui suatu platform yang interaktif, seperti halnya teknologi augmented reality. Penelitian ini bertujuan membangun Aplikasi Augmanted reality berbasis mobile menggunakan metode markerless sebagai media promosi, mempermudah pelanggan, menghemat waktu dan biaya. Sistem dibangun menggunakan metode pengembangan waterfall dengan memanfaatkan beberapa perangkat lunak tambahan, yakni Unity 3D, Sketchup dan Blender. Kelayakan sistem berhasil memperoleh penilaian dengan predikat Sangat Baik yang merupakan representasi dari Skala Likert dari pengujian ISO 25010 yang mencakup aspek functional suitability mencapai 100% dan aspek usability mencapai 91.2%.
Sentiment Analysis of COVID-19 Booster Vaccines on Twitter Using Multi-Class Support Vector Machine Nurkholis, Andi; Styawati, Styawati; Alim, Syahirul; Saputra, Hendi; Ferriyan, Andrey
Applied Information System and Management (AISM) Vol. 8 No. 1 (2025): Applied Information System and Management (AISM)
Publisher : Depart. of Information Systems, FST, UIN Syarif Hidayatullah Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15408/aism.v8i1.42911

Abstract

The Indonesian government's implementation of a booster vaccination program as part of its COVID-19 response has generated diverse public reactions, particularly on social media platforms like Twitter. This study aims to analyze public sentiment regarding booster vaccines by examining Twitter data to understand the prevailing discourse and attitudes toward this policy. The research employs sentiment analysis, a text mining and processing technique, to classify tweets into positive, neutral, and negative categories. The study utilizes the Support Vector Machine (SVM) algorithm, evaluating its performance through a multi-class parameter assessment. Two multi-class strategies, One-against-one (OAO) and One-against-rest (OAR) are combined with various kernels (Sigmoid, Polynomial, and RBF) to identify the most accurate model for sentiment classification. The results show that the OAO method with the RBF kernel achieves the highest accuracy of 96%, outperforming other combinations like OAO with Polynomial (95.2%) and Sigmoid (93.7%) kernels. Similarly, the RBF kernel performs best with 95.5% accuracy in the OAR approach. Using the optimal model, sentiment analysis classifies 49 tweets as positive, 927 as neutral, and 24 as negative, revealing a predominantly neutral public sentiment with limited positive and negative opinions. In conclusion, this study demonstrates the effectiveness of SVM, particularly the OAO method with the RBF kernel, for sentiment analysis of social media data. The findings provide insights into public perceptions of the booster vaccine program, offering policymakers a data-driven basis for designing targeted communication strategies to address concerns and enhance public acceptance.
Cocoa Land Suitability Analysis Using ID3 Spatial Algorithm Nurkholis, Andi; Rahayu, Ririn Wuri; Ferriyan, Andrey; Ni’mawati, Akfina
Applied Information System and Management (AISM) Vol. 8 No. 2 (2025): Applied Information System and Management (AISM)
Publisher : Depart. of Information Systems, FST, UIN Syarif Hidayatullah Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15408/aism.v8i2.46681

Abstract

Cocoa production in Indonesia encounters ongoing challenges due to declining plantation areas and suboptimal land utilization. This study applies the ID3 Spatial algorithm to evaluate land suitability for cocoa cultivation in Bogor Regency, West Java Province. The methodology integrates nine basic land characteristics, including elevation, drainage, relief, base saturation, cation exchange capacity, soil texture, soil pH, and mineral soil depth, derived from field surveys conducted by BBSDLP. Two classification models were developed and tested using spatial data preprocessing techniques. Model M1 was the baseline approach without constraints, while Model M2 incorporated a minimum planted area threshold of ≥1 ha. The results show notable performance differences between models. Model M2 achieved a reasonable accuracy of 87.27% compared to Model M1’s 29.09%, with relief identified as the root node due to its higher gain value and reduced entropy. Classification results indicate that Bogor Regency’s cocoa cultivation potential comprises 16,443 ha of S2 (moderately suitable) land and 231,018 ha of S3 (marginally suitable) land. The generated land suitability map may provide stakeholders with helpful guidance for identifying potential cultivation areas. The result suggests that artificial intelligence integration, specifically the ID3 spatial algorithm, could improve land suitability evaluation processes, potentially supporting more informed agricultural development decisions.
Prediction Model for Soybean Land Suitability Using C5.0 Algorithm Nurkholis, Andi; Styawati, Styawati
JOIN (Jurnal Online Informatika) Vol 6 No 2 (2021)
Publisher : Department of Informatics, UIN Sunan Gunung Djati Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15575/join.v6i2.711

Abstract

Soybean is one of the protein main sources that can be used for consumption in tempeh, tofu, milk, etc. Based on projection results, soybean production and consumption balance in Indonesia, in 2018-2022, it is estimated that deficit will increase by 6.18% per year. So, it's necessary to guide soybean land suitability, which can be carried out by evaluating existing land suitability to support soybean farming expansion and production. This study conducted an analytical study to evaluate soybean land suitability using C5.0 algorithm based on land and weather characteristics. The C5.0 algorithm is an extension of spatial decision tree, an ID3 decision tree extension. Dataset is divided into two categories: explanatory factors representing seven land characteristics (drainage, land slope, base saturation, cation exchange capacity, soil texture, soil pH, and soil mineral depth) and two weather data (rainfall and temperature), and a target class represent soybean land suitability in two study areas, namely Bogor and Grobogan Regency. The result generated two land suitability models with the best model obtained accuracy for training data 98.58%, while testing data was 97.17%. The best model rules are 69 rules that do not involve three attributes: cation exchange capacity, soil mineral depth, and rainfall.
Penerapan Aplikasi Mobile Company Profile Tambang Batu Andesit Pada CV Handika Karya Nurkholis, Andi; Sakti, Hakim Erlangga Bernado; Rahayu, Ririn Wuri; Firmansyah, Ilham
Journal of Social Sciences and Technology for Community Service (JSSTCS) Vol 6, No 1 (2025): Volume 6, Nomor 1, March 2025
Publisher : Universitas Teknokrat Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33365/jsstcs.v6i1.4909

Abstract

Di era digital saat ini, kehadiran teknologi mobile telah mengubah cara perusahaan berkomunikasi dengan pelanggan dan pemangku kepentingan. CV. Handika Karya, sebuah perusahaan penambangan batu andesit yang masih menjalankan sistem manajemen secara konvensional, dapat memanfaatkan aplikasi mobile company profile untuk meningkatkan visibilitas, mempromosikan praktik penambangan yang berkelanjutan, mengedukasi masyarakat, serta meningkatkan daya saing. Kegiatan pengabdian kepada masyarakat yang dilakukan oleh tim dosen Universitas Pembangunan Nasional "Veteran" Yogyakarta bertujuan untuk menerapkan aplikasi mobile company profile sebagai solusi strategis dalam menghadapi tantangan digitalisasi. Melalui tahapan observasi, pelaksanaan, dan pelaporan, tim PKM mengembangkan aplikasi yang memungkinkan CV. Handika Karya menampilkan informasi penting seperti profil perusahaan, produk batu andesit, proses penambangan ramah lingkungan, serta program tanggung jawab sosial secara digital. Implementasi aplikasi ini memberikan manfaat signifikan termasuk peningkatan transparansi, kemudahan akses informasi, partisipasi masyarakat dalam pengawasan, edukasi industri tambang, pengembangan ekonomi lokal, dan peningkatan kualitas komunikasi. Kegiatan ini mendapat respons sangat positif dari perusahaan, ditunjukkan dengan partisipasi aktif karyawan dan penyediaan fasilitas yang diperlukan, serta terbukti efektif berdasarkan hasil kuesioner kepuasan dan kemudahan penggunaan. Bagi tim dosen, program ini menjadi wadah untuk mengaplikasikan ilmu pengetahuan dan berkontribusi kepada masyarakat melalui pengembangan solusi teknologi informasi yang inovatif.
Firefly Algorithm for SVM Multi-class Optimization on Soybean Land Suitability Analysis Nurkholis, Andi; Styawati, Styawati; Suhartanto, Alvi
JOIV : International Journal on Informatics Visualization Vol 8, No 2 (2024)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.8.2.1860

Abstract

Soybean is the primary source of vegetable protein nutrition, containing fat and vitamins that Indonesian people widely consume. The decline in soybean production in Indonesia every year is due to the reduced area of soybean cultivation, thereby increasing dependence on imports from other countries. Land suitability maps can provide directions for priority locations for soybean cultivation based on land characteristics and weather to produce optimal production. The SVM multi-class algorithm has been applied to classify land suitability data to create a land suitability map but has yet to obtain optimal accuracy, especially for sigmoid kernels. The objective of this study is to enhance the performance of the sigmoid kernel SVM by utilizing the firefly algorithm. The study focuses on evaluating the suitability of soybean cultivation in Bogor and Grobogan Regencies. The results of the tests indicate that the firefly algorithm-optimized SVM (FA-SVM) significantly improves accuracy compared to the SVM without optimization. The accuracy achieved by FA-SVM is 89.95%, while the SVM without optimization only achieves an accuracy of 65.99%. The best parameters produced by the firefly algorithm are C=2.33 and σ=0.45 obtained from firefly customization, and the number of generations is 10. Based on this, the optimization algorithm can be used to produce an optimal model. The best optimal model obtained can be used as a guide for priority locations/areas for soybean cultivation by farming communities, so as to produce maximum soybean productivity.
Analisis Sentimen Masyarakat Indonesia Terkait Vaksin Covid-19 Pada Media Sosial Twitter Menggunakan Metode Support Vector Machine (Svm) Arfat, Muhammad Fadilah; Styawati, Styawati; Nurkholis, Andi; Kurniawan, Indra
Jurnal Informatika: Jurnal Pengembangan IT Vol 7, No 2 (2022)
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/jpit.v7i2.3549

Abstract

COVID-19 is a new disease outbreak that has been officially designated as a global pandemic by the Worldi Health Organizationi (WHO) oni March 11, 2020. Seeing the rapid development of COVID-19, the Government of Indonesia has carried out vaccinations that have been carried out since January 13, 2021, this vaccination is prioritized for medical personnel and red zone areas. Since its emergence, therei have been many prosi andi consi regardingi the vaccination process and it has alsoi become a trending topici on sociali media Twitter oni January 13, 2021. Onei of the mosti widely used social media by Indonesiani people isi twitter sociali media. According to We arei Social sources in 2020, twitteri social media is rankedi fifth in the category of sociali media that is often used with a user percentage of 56% after Youtube, Whatsapp, Facebook as well as Instagram. Thisi shows that there is a huge opportunity for data sources that can be usedi to find out the positive and negativei sentiments of the related community, which is useful for interested parties to carry out evaluations. So that it can see how many people agree and disagree. If the percentage of people who disagree is more, the government must do better socialization so that people can better understand and not feel afraid of the vaccine. This study aims to find out how public sentiment is about the government's policies regarding the COVID-19 vaccinei using the Support Vector Machine method. by extracting the tf-idf feature and comparing the kernels contained in the SVM, including Linear, RBF, Polynomial, and Sigmoid. With tests that will later see how the values of accuracy, precision, recall and F1-Score are. 
LITERASI ARTIFICIAL INTELLIGENCE UNTUK GURU MADRASAH ALIYAH KUN SHOLIHAN Rudy Cahyadi; Budi Suyanto; Andi Nurkholis; Andrey Ferriyan
Jurnal Padamu Negeri Vol. 2 No. 4 (2025): Oktober : Jurnal Padamu Negeri (JPN)
Publisher : CV. Denasya Smart Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.69714/e3nx7t82

Abstract

The development of artificial intelligence (AI) technology has brought about significant changes in various aspects of life, including education. AI is now used not only in industry and business but has also penetrated classrooms as a learning aid, personalized material, and even as a learning evaluation system. However, the use of this technology is not yet fully understood and optimally implemented by educators, especially in madrasah environments. Therefore, an AI literacy training program specifically designed for teachers at Madrasah Aliyah Kun Sholihan is needed. This training is expected to improve teachers' understanding of the basic concepts of AI, its application in learning contexts, and practical skills in using various AI-based platforms or applications to support more interactive, effective, and adaptive teaching and learning activities.
MODEL KLASIFIKASI PENERIMA PROGRAM KARTU INDONESIA PINTAR MENGGUNAKAN METODE XGBOOST Nurkholis, Andi; Styawati, Styawati; Susi, Susi; Munawar, Alifah Chairul
INTI Nusa Mandiri Vol. 20 No. 2 (2026): INTI Periode Februari 2026
Publisher : Lembaga Penelitian dan Pengabdian Pada Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/inti.v20i2.7770

Abstract

Poverty is one of the major factors contributing to the low quality of education in Indonesia. The Smart Indonesia Program (Program Indonesia Pintar) is a cash assistance program distributed through the Smart Indonesia Card (Kartu Indonesia Pintar/KIP) to support students from economically disadvantaged families, ranging from elementary to higher education levels. This study aims to classify students who are eligible to receive the Smart Indonesia Card using the XGBoost method, with a case study conducted at SMPN 02 Kebun Tebu. The dataset used in this study consists of independent and dependent variables. The independent variables include father’s age, mother’s age, father’s education level, mother’s education level, father’s income, mother’s income, number of family dependents, and students’ academic average scores. The dependent variable is the eligibility status of KIP recipients as the target class. Two classification models were developed using data split ratios of 70:30 and 80:20. The model with a 70:30 data split achieved an accuracy of 0.9048, a precision of 0.9034, a recall of 0.9072, and an F1-score of 0.9053. Meanwhile, the model with an 80:20 data split demonstrated better performance, with an accuracy of 0.9167, a precision of 0.9149, a recall of 0.9189, and an F1-score of 0.9169. The optimal model obtained from this study can be utilized by schools to support policy decision-making in determining eligible Smart Indonesia Card recipients, ensuring that educational assistance is distributed accurately, adequately, and equitably
Co-Authors 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 Ferriyan, Andrey Anjumi, Krisma Nur Annisa Annisa Ans, Faris Arkan Arfat, Muhammad Fadilah Arief Budiman Aris Munandar Bagas Aditama Bagus Miftaq Hurohman Berlintina Permatasari Budi Suyanto 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 Munawar, Alifah Chairul Nadia Aftirah Nadiya Safitri Neneng Neneng Ni’mawati, Akfina Oktora, Putri Suci Pasaribu, A. Ferico Octaviansyah Pasha, Donaya Prasetyo, Aditya Dwi Pria Agung Laksono Priandika, Adhie Thyo 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 susi susi 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