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All Journal Dinamik Techno.Com: Jurnal Teknologi Informasi JSI: Jurnal Sistem Informasi (E-Journal) CESS (Journal of Computer Engineering, System and Science) Jurnal ELTIKOM : Jurnal Teknik Elektro, Teknologi Informasi dan Komputer Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) RABIT: Jurnal Teknologi dan Sistem Informasi Univrab JITK (Jurnal Ilmu Pengetahuan dan Komputer) JURNAL TEKNOLOGI DAN ILMU KOMPUTER PRIMA (JUTIKOMP) STRING (Satuan Tulisan Riset dan Inovasi Teknologi) Journal of Information System, Applied, Management, Accounting and Research International Journal of Informatics and Computation JATI (Jurnal Mahasiswa Teknik Informatika) JISA (Jurnal Informatika dan Sains) REMIK : Riset dan E-Jurnal Manajemen Informatika Komputer Jurnal Sistem Informasi dan Sains Teknologi Jurnal Teknologi Informatika dan Komputer Journal of Computer Networks, Architecture and High Performance Computing Jurasik (Jurnal Riset Sistem Informasi dan Teknik Informatika) Bulletin of Information Technology (BIT) Pelita Teknologi : Jurnal Ilmiah Informatika, Arsitektur dan Lingkungan Jurnal Ilmiah SIGMA: Informatics Engineering Journal of UPB Joong-Ki : Jurnal Pengabdian Masyarakat Joutica : Journal of Informatic Unisla Journal of Practical Computer Science (JPCS) Prosiding Seminar Nasional Sisfotek (Sistem Informasi dan Teknologi Informasi) Malcom: Indonesian Journal of Machine Learning and Computer Science Riwayat: Educational Journal of History and Humanities VIDHEAS: Jurnal Nasional Abdimas Multidisiplin SAINTEK Joong-Ki JPMAS : Jurnal Pengabdian Masyarakat Dedikasi : Jurnal Pengabdian Lentera RECORD Journal of Loyality and Community Development Jurnal ilmiah teknologi informasi Asia Joong-Ki
Agung Nugroho
Universitas Pelita Bangsa
Religion Aerospace Engineering Agriculture, Biological Sciences & Forestry Humanities Automotive Engineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Social Sciences Other

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Analisis Sentimen Tentang Mobil Listrik Dengan Metode Support Vector Machine Dan Feature Selection Particle Swarm Optimization Ahmad Santoso; Agung Nugroho; Aswan S Sunge
Journal of Practical Computer Science Vol. 2 No. 1 (2022): Mei 2022
Publisher : DPPM Universitas Pelita Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37366/jpcs.v2i1.1084

Abstract

Analisis sentimen twitter merupakan teknik untuk mengidentifikasi sentimen atau pendapat dalam tweet dan kemudian mengategorikannya ke dalam tweet positif atau tweet negatif salah satu topik yang dibahas pada social media twitter adalah mobil listrik, mobil listrik memiliki beberapa kelebihan dibandingkan dengan mobil bahan bakar fosil. Mobil listrik ini menuai banyak komentar dari masyarakat sehingga menimbulkan pro dan kontra di sosial media twitter. Penelitian ini dilakukan tujuannya untuk mengetahui pendapat masyarakat terhadap mobil listrik. Apakah pendapat tersebut lebih mengarah ke positif atau negatif dan untuk mengetahui nilai accuracy, AUC dari penggunaan metode Support Vector Machine dan feature selection Particle Swarm Optimization pada Software RapidMiner Studio. di dalam penelitian ini dapat diketahui bahwa 94,25% pengguna twitter setuju dan 5,75% pengguna twitter tidak setuju terhadap kehadiran mobil listrik. Penggunaan feature selection Particle Swarm Optimization pada metode support vector machine untuk menganalisis sentimen masyarakat mengenai mobil listrik dapat meningkatkan nilai accuracy dan AUC. Dimana nilai accuracy yang awalnya sebesar 82,51% menjadi 86,07%, terjadi kenaikan sebesar 3,56%. Sedangkan nilai AUC yang awalnya sebesar 0,844 menjadi 0,862 terjadi kenaikan sebesar 2,13%. Kata kunci: Analisis Sentimen, Text Mining, Support Vector Machine, Particle Swarm Optimization, Mobil Listrik.
Penerapan data mining menggunakan algoritma C4.5 dalam prediksi penyakit angin duduk Salman Alfaridzi; Agung Nugroho; Muhammad Rizki Sani
Jurnal Sistem Informasi Vol 13, No 2 (2021)
Publisher : Universitas Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (3056.06 KB) | DOI: 10.36706/jsi.v13i2.15638

Abstract

ABSTRAKPenyakit angin duduk (Angina Pectoris) merupakan penyakit yang terjadi karena gangguan pada aliran darah menuju jaringan otot jantung yang menyebabkan terjadinya nyeri pada dada. Angin duduk terjadi karena adanya penyempitan pembuluh coroner yang menyebabkan suplai oksigen untuk otot jantung mengalami gangguan sehingga jantung tidak dapat memompa darah dengan maksimal. Kurangnya pengetahuan masyarakat dalam mendeteksi gejala penyakit ini maka dengan memanfaatkan data tersebut penulis ingin menerapkan salah satu teknik data mining dalam melakukan prediksi atau mendiagnosis penyakit angin duduk (angina pectoris). Metode yang digunakan adalah Algoritma C4.5 dan Particle Swarm Optimization (PSO) dengan alat bantu RapidMiner dengan menggunakan sebanyak 200 data. Hasil analisis menunjukkan bahwa gejala kolestrol, diabetes, hipertensi, obesitas dan merokok bisa menjadi indikator untuk mendiagnosis penyakit angin duduk (angina pectoris). Hasil nilai yang didapatkan dari penelitian ini yaitu nilai Accuracy yang didapatkan meningkat sebanyak 7,5% dari 76,50% menjadi 84,00%, nilai Precision yang didapatkan meningkat sebanyak 7,64% dari 80,50% menjadi 88,14%, dan nilai Recall yang didapatkan meningkat sebanyak 9% dari 72,00% menjadi 81,00%. Kata Kunci: Data Mining, Angin Duduk, Algoritma C4.5, Particle Swarm Optimization, RapidMiner ABSTRACKSitting wind disease (Angina Pectoris) is a disease that occurs due to disruptions in blood flow to heart muscle tissue that causes chest pain. Wind sitting occurs due to a narrowing of the coroner vessels that cause the oxygen supply to the heart muscle to be disrupted so that the heart cannot pump blood optimally. Lack of public knowledge in detecting the symptoms of this disease then by utilizing the data the author wants to apply one of the data mining techniques in predicting or diagnosing sitting wind disease (angina pectoris). The methods used are Algorithm C4.5 and Particle Swarm Optimization (PSO) with RapidMiner tools using as much as 200 data. The results of the analysis showed that the symptoms of cholesterol, diabetes, hypertension, obesity and smoking could be indicators for diagnosing sitting wind disease (angina pectoris). The results of the value obtained from this study are that the accuracy value obtained increased by 7.5% from 76.50% to 84.00%, the precision value obtained increased by 7.64% from 80.50% to 88.14%, and the recall value obtained increased by 9% from 72.00% to 81.00%. Kata Kunci: Data Mining, Sitting Wind, C4.5 Algorithm, Particle Swarm Optimization, RapidMiner.
Sistem Pendukung Keputusan Menentukan Siswa Yang Menerima Beasiswa Menggunakan Metode SAW Jamaludin Jamaludin; Agung Nugroho; Ikhsan Romli
Prosiding SISFOTEK Vol 4 No 1 (2020): Vol 4 No 1 (2020): SISFOTEK 2020
Publisher : Ikatan Ahli Informatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (437.588 KB)

Abstract

The number of schools that are growing steadily, making the school is required to implement a better strategy. For that reason, the school must observe all the activities that exist in the school environment to be able to attract the attention of parents so that their children attend school. Decision Support System is basically to facilitate the school in selecting candidates who will receive scholarships, to get more accurate results, and apply the Simple Additive Weighting method. At SD Negeri Singajaya 03 the process of determining students who receive scholarships uses several criteria, namely Parental Income, Semester, Parental Dependency, Number of Brothers, and Value. In building this system the author uses the System Development Life Cycle (SDLC) development method, and for system design using the Unified Modeling Language (UML). For making the application the author uses PHP programming language and MYSQL database testing using the Black Box Testing method. The results of this study are an application of decision support to determine prospective scholarship recipients using the method Simple Additive Weighting (SAW). In conclusion, this Decision Support System is more convincing than the old method. Because the calculation results are faster, more efficient, and more accurate.
Analisis Sentimen Terhadap Pembobolan Data pada Twitter dengan Algoritma Naive Bayes Ahmad Turmudi Zy; Agung Nugroho; Ahmad Rivaldi; Irfan Afriantoro
Jurnal Teknologi Informatika dan Komputer Vol 8, No 2 (2022): Jurnal Teknologi Informatika dan Komputer
Publisher : Universitas Mohammad Husni Thamrin

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37012/jtik.v8i2.1240

Abstract

Perkembangan teknologi informasi kini sangat cepat dan jauh berbeda dengan masa awal kehadirannya. Era globalisasi telah menempatkan peranan teknologi informasi ke dalam suatu posisi yang sangat strategis karena dapat menghadirkan suatu dunia tanpa batas, jarak, ruang, dan waktu serta dapat meningkatkan produktivitas serta efisiensi. Twitter merupakan media sosial yang mudah digunakan untuk penyebaran informasi secara cepat dan luas. Sejak ramainya kasus Bjorka hal itu memicu banyak masyarakat yang mengkritik di berbagai media sosial salah satu diantaranya media sosial Twitter sehingga kritik atau opini tersebut dapat dimanfaatkan untuk melakukan analisis sentimen. Berdasarkan hal tersebut, diperlukan sebuah metode yang dapat secara otomatis melakukan klasifikasi opini ke dalam kategori positif dan negatif melalui proses analisis sentimen. Proses analisis sentimen dilakukan dengan proses data preprocessing, pembobotan kata menggunakan metode TF-IDF, penerapan algoritma, dan pembahasan atas hasil klasifikasi. Metode klasifikasi data yang digunakan dalam penelitian ini adalah Naive Bayes Classifier (NBC). Data tersebut akan diproses menggunakan text mining dan klasifikasi menggunakan algoritma Naive Bayes. Metode tersebut menghasilkan tingkat dan hasil yang cukup baik. Klasifikasi dapat memberikan kemudahan bagi pengguna untuk melihat opini positif dan negatif. Berdasarkan pengujian yang telah dilakukan, hasil klasifikasi terbaik diperoleh dengan nilai accuracy, precision, dan recall tertinggi yang mendapatkan hasil dengan nilai accuracy 98.33%, precision 100.00%, dan recall sebanyak 97.13%.
Sistem Pakar Corrective Bay Penghantar Gardu Induk Mekarsari Karawang Dengan Metode Forward Chaining Agung Nugroho
Jurnal SIGMA Vol 12 No 2 (2021): Juni 2021
Publisher : Teknik Informatika, Universitas Pelita Bangsa

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Abstract

The need for electrical and technological energy at this time has grown so rapidly that electrical energy becomes one of the main needs in life. In the course of the substation itself in distributing power supply many disturbances that sometimes disrupt the distribution of electricity in factors such as equipment age and external factors such as lightning and so on. So in need of a system that can help in overcoming the disruption that occurred. The research method used is Quesioner methodology, Interview or Q & A, analysis which includes making flowcharts, and in system design include making State Transition Diagram (STD) and proposed proposal system design (Msukan Design, Process and Output). This study contains about the design of expert systems to find a solution of the disturbances that occur in the bay Deliver by diagnosing the symptoms that arise on the substation. This system is a new system design that is used to find the right disturbance solution, fast, and efficient. The author in this case designed the system by mengguanakan forward chaining method as for this application is made with PHP programming language and using MySQL Database. With this expert system is expected to mengefisiensikan various things such as communication, time and so forth in handling disruptions that occur. Keywords: Expert System, Disturbance, Indication
Mencegah Kredit Macet Dengan Analisa Kelayakan Pembiayaan Dengan Metode C4.5 Dan Naïve Bayes (Studi Kasus : Koperasi BMT UGT Sidogiri Cabang Cikarang) Agung Nugroho
Jurnal SIGMA Vol 11 No 2 (2020): Juni 2020
Publisher : Teknik Informatika, Universitas Pelita Bangsa

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Abstract

The progress of the growth of MSMEs (Micro, Small and Medium Enterprises) in Indonesia from time to time which is increasingly rapid, resulting in an increase in the need for capital to develop their business. This is evidenced by the increasing number of credit or financing withdrawals from savings and loan cooperatives and BPRs (Rural Banks). The problem faced by savings and loan cooperatives, BPRs, or other financial institutions at this time in providing credit is the risk of late payments, repayments and even failure of credit payments. This problem occurs due to credit misuse and weak supervision both in the process of providing credit and in the implementation stage. The right solution to solve existing problems is by using data mining algorithms. The concept of data mining will make it easier to solve problems that are not optimal in cooperatives, the classification method is able to find models that differentiate concepts or data classes with the aim of making it easier to predict creditworthiness. The Naive Bayes algorithm and the C4.5 algorithm are considered to be able to predict future opportunities based on previous experiences. The author conducted research on the BMT UGT Sidogiri Cooperative with the title "Preventing bad credit by analyzing the feasibility of financing with the Naive Bayes and C4.5 methods". In this study the authors used 9 attributes as an assessment, namely: name, residence status, financing contract, income, ceiling, term of repayment, number of dependents, collateral. Testing is done using 520 data and 104 randomly selected testing data. From the results of tests carried out using Rapid Miner tools, it can be concluded that the accuracy level of the C4.5 algorithm is more accurate at 81.35%, while the Naive Bayes algorithm is 78.85%. Keywords : Credits, Classification, Accuracy, Naive Bayes, C4.5.
Optimasi Parameter Support Vector Machine dengan Algoritma Genetika Untuk Penilaian Resiko Kredit Agung Nugroho; Arif Tri Widiyatmoko
Jurnal Pelita Teknologi Vol 17 No 2 (2022): September 2022
Publisher : DPPM Universitas Pelita Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37366/pelitatekno.v17i2.1537

Abstract

The aim of this study is to optimize the parameters of a Support Vector Machine (SVM) using a genetic algorithm for credit risk assessment. Consumer credit data from a bank is used in this research. The results show that the SVM with parameters optimized using a genetic algorithm provides better classification performance compared to the SVM with default parameters. In addition, the genetic algorithm can also quickly and efficiently optimize SVM parameters. In conclusion, the genetic algorithm can be used to optimize SVM parameters for credit risk assessment Keywords: Support Vector Machine (SVM), Parameter optimization, Genetic algorithm, Credit risk assessment, Classification performance
Sistem Pendukung Keputusan Penentuan Penerimaan Beasiswa Sma Negeri 1 Serang Baru Kabupaten Bekasi Menggunakan Metode Simple Additive Weighting (SAW) Agung Nugroho; Alfatan Dzulatkha
Jurnal SIGMA Vol 10 No 3 (2019): September 2019
Publisher : Teknik Informatika, Universitas Pelita Bangsa

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

Abstract

Pendidikan pada masa sekarang memang dianggap sangat penting sehingga negara sangat mendukung setiap warga negaranya untuk meraih pendidikan setinggitingginya. Saat ini SMA Negeri 1 Serang Baru belum diterapkan suatu metode dalam membantu menyeleksi siswa penerima beasiswa. Beasiswa dapat dikatakan sebagai pembiayaan yang tidak bersumber dari pendanaan sendiri atau orang tua, akan tetapi diberikan oleh pemerintah, perusahaan swasta, kedutaan, universitas, serta lembaga pendidik atau peneliti, namun banyak beasiswa yang dirasa kurang tepat sasaran sehingga perlu adanya suatu sistem pendukung keputusan guna meminimalisir kesalahan pemberian beasiswa. Sistem pendukung keputusan yang akan dibangun menggunakan metode Simple Additive Weighting. Dalam membangun sistem ini menggunakan metode pengembangan sistem model Waterfall, desain sistem menggunakan Unified Modelling Language pengujiannya menggunakan metode pengujian BlackBox dan untuk implementasi sistem menggunakan bahasa pemograman web, PHP, Javascript dan database MySQL. Hasil dari penelitian ini adalah sebuah sistem pendukung keputusan penerimaan beasiswa menggunakan metode SAW dan telah berhasil di implementasikan, diharapkan dengan adanya sistem tersebut dapat menjadi alternatif metode pengambilan keputusan dalam proses menyeleksi penerimaan beasiswa. Kata Kunci : Simple Additive Weighting, Sistem Pendukung Keputusan, Beasiswa Kata Kunci: Simple Additive Weighting, Sistem Pendukung Keputusan, Beasiswa
Analisa Data Mining Untuk Prediksi Penyakit Ginjal Kronik Dengan Algoritma Regresi Linier Angga Kurniadi Hermawan; Agung Nugroho; Edora
Bulletin of Information Technology (BIT) Vol 4 No 1: Maret 2023
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bit.v4i1.475

Abstract

In this study, we evaluate the ability of data mining to predict chronic kidney disease using a linear regression algorithm. We extract features from patient clinical data and apply a linear regression algorithm to build a predictive model. The results showed that our linear regression model was able to predict with high accuracy and could be used as an aid in diagnosing chronic kidney disease. In addition, we also analyze the factors that influence the risk of developing chronic kidney disease and suggest preventive measures that can be taken to reduce the risk of developing the disease. The results of this study can be used by doctors to improve efficiency in diagnosing and preventing chronic kidney disease. In addition, these results can also be used as a basis for further research in the field of data mining and chronic kidney disease. The process of testing the data in this study using a linear regression algorithm is able to provide good results with a Root Mean Squared Error: 0.285 +/- 0.000 and Squared Error: 0.081 +/- 0.234.
Prediksi Penyakit Kanker Paru-Paru Dengan Algoritma Regresi Linier Muhammad Abdul Rahman Wahid; Agung Nugroho; Abdul Halim Anshor
Bulletin of Information Technology (BIT) Vol 4 No 1: Maret 2023
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bit.v4i1.501

Abstract

Lung cancer is one of the deadliest types of cancer worldwide. Therefore, efforts to predict the likelihood of developing lung cancer are very important in its prevention and treatment. One way to predict the likelihood of getting lung cancer is to use a linear regression algorithm. This study aims to develop a predictive model that can identify a person's likelihood of developing lung cancer based on certain factors, such as age, passive smoker and level or severity. The data used in this study were collected from 100 patients diagnosed with lung cancer and their severity. The results of the analysis show that the linear regression algorithm can be used to predict the probability of getting lung cancer with an accuracy of about 90% and is able to give good results with a Root Mean Squared Error: 0.686 +/- 0.000 and Squared Error: 0.471 +/- 0.546
Co-Authors Abdul Halim Anshor Adi Kusuma Adi Kusuma Afrizal Ardiansyah Agus Suwarno, Agus Ahmad Rivaldi Ahmad Santoso Ahmad Turmudi Zy Ahmad Turmudi Zy Ahmad Turmudi Zy Al Abdilah, M. Akmal Alfatan Dzulatkha Alviyan, Eric Ananto Tri Sasongko Angga Kurniadi Hermawan Ansor, Abdul Halim Ari Maulana Arif Tri Widiyatmoko Aswan S Sunge Aswan Supriyadi Sunge Ayu Indriyanti Bima Randy Gumilang Butsianto, Sufajar Chandra, Ahmad Alfian Donny Maulana Edora Elkin Rilvani Endrik Fauzi Ahmad Muda Fitri, Retno Gatot Tri Pranoto Ike Yunia Pasa Ikhsan Romli Ilyas, Abdi Irfan Afriantoro Jamaludin Jamaludin Londong, Riko Reky Maulana, Donny Muhammad Abdul Rahman Wahid Muhammad Rizki Sani Nisa, Khilyatun Nur Hidayati Nur Hidayati Nurjeli Nurjeli Priatna , Wowon Rusdi, Adi Salman Alfaridzi Sasongko, Ananto Tri Septiawan Dwi Pramukti Supriadi, Asmadi Turmudi Zy, Ahmad Wahyono Wahyono Wahyu Hadikristanto Widiyatmoko, Arif Tri Wiyanto Wiyanto Wiyanto - Wiyanto Wiyanto Wiyanto, Wiyanto Yoga Religia