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All Journal Jurnal Pendidikan Teknologi dan Kejuruan Voteteknika (Vocational Teknik Elektronika dan Informatika) Proceedings of KNASTIK Prosiding Seminar Nasional Sains Dan Teknologi Fakultas Teknik Jurnal Pekommas Jurnal Edukasi dan Penelitian Informatika (JEPIN) Infotech Journal Sistemasi: Jurnal Sistem Informasi Jurnal Ilmiah Matrik BAREKENG: Jurnal Ilmu Matematika dan Terapan Matrix : Jurnal Manajemen Teknologi dan Informatika JITTER (Jurnal Ilmiah Teknologi Informasi Terapan) INTECOMS: Journal of Information Technology and Computer Science KOMIK (Konferensi Nasional Teknologi Informasi dan Komputer) JURIKOM (Jurnal Riset Komputer) JUMANJI (Jurnal Masyarakat Informatika Unjani) Jurnal Teknologi Terpadu Jurnal Informatika dan Rekayasa Elektronik JATI (Jurnal Mahasiswa Teknik Informatika) Tematik : Jurnal Teknologi Informasi Komunikasi Jurnal Teknik Informatika (JUTIF) Informatics and Digital Expert (INDEX) Jurnal Informatika dan Teknologi Komputer ( J-ICOM) Jurnal Sosial dan Teknologi Jurnal Locus Penelitian dan Pengabdian Jurnal Informatika Teknologi dan Sains (Jinteks) Prosiding Seminar Nasional Teknik Elektro, Sistem Informasi, dan Teknik Informatika (SNESTIK) Prosiding Seminar Nasional Sisfotek (Sistem Informasi dan Teknologi Informasi) Jurnal Algoritma J-Icon : Jurnal Komputer dan Informatika IJESPG (International Journal of Engineering, Economic, Social Politic and Government) journal Enrichment: Journal of Multidisciplinary Research and Development RESLAJ: Religion Education Social Laa Roiba Journal In Search (Informatic, Science, Entrepreneur, Applied Art, Research, Humanism) Seminar Nasional Riset dan Teknologi (SEMNAS RISTEK)
Yulison Herry Chrisnanto
Program Studi Informatika, Fakultas Sains Dan Informatika Universitas Jenderal Achmad Yani
Religion Aerospace Engineering Arts 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 Energy Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mathematics Mechanical Engineering Physics Social Sciences Transportation Other

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Segmentasi Loyalitas Pelanggan Berbasis RFM (Recency, Frequency, Monetary) Menggunakan K-Means pada PD. Persada Ikan Yosia Oktavian Pailan; Yulison Herry Chrisnanto; Asep Id Hadianna
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 (457.406 KB)

Abstract

Customer loyalty for the company is very important if the competition between similar companies is high enough which results in a threat to the company. Customer loyalty is very useful to determine the level of customer loyalty to the company. Customer segmentation is also needed to group customers who have the same characteristics into one so as to simplify the management process. The analysis used is the RFM (Recency, Frequency, Monetary) model to analyze customer buying behavior in terms of Recency (last transaction time span), Frequency (number of transactions), and Monetary (rupiah issued). The grouping method used is K-Means. The data used in this study are historical data on fish feed purchases from 2015 to 2017. The application of RFM analysis and the K-Means method resulted in 4 clusters based on Elbow calculations. The results in this study obtained the number of objects in cluster 1 as many as 142 customers, cluster 2 as many as 28 customers, cluster 3 as many as 41 customers and cluster 4 as many as 41 customers. The accuracy level of the cluster is measured using Silhoutte Coeffisien with results close to 1 which means the clustering is quite good. The interpretation of the RFM shows that 16.27% of customers have a high potential for loyalty, while 11.11% of customers have the potential as loyal customers, and the remaining 56.35% have a low level of loyalty. It can be concluded that this research can classify the level of customer loyalty using RFM analysis and the K-Means algorithm.
Sistem Prediksi Mutu Air Di Perusahaan Daerah Air Minum Tirta Raharja Menggunakan K – Nearest Neighbors (K – NN) Rahandanu Rachmat; Yulison Herry Chrisnanto; Fajri Rakhmat Umbara
Prosiding SISFOTEK Vol 4 No 1 (2020): Vol 4 No 1 (2020): SISFOTEK 2020
Publisher : Ikatan Ahli Informatika Indonesia

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Abstract

PDAM (Perusahaan Daerah Air Minum) Tirta Raharja is the only Regional Business Entity (BUMD) that has the task of providing clean water services to the people of Cimahi City. Clean water is the main requirement that must be consumed by the community and managed in the smooth running of community activities. The development of the city of Cimahi is currently quite fast, with plans to build smart cities, causing the need for clean air as needed. K - Nearest Neighbor (KNN) is a classification algorithm that considers several supporting parameters to carry out a classification process that results in ease of calculation and power. KNN can be considered as one of the most famous non-parametric models. In the research and implementation process of data mining in the regulation of water quality feasibility in PDAM Tirta Raharja using K - the nearest neighbor can be denied as the K - the nearest neighbor implemented in the process of testing the drinking water feasibility in PDAM Tirta Raharja, can be used 93% to be used with the Eligible label Drunk, and 98% for accuracy testing with the label Not Eligible to drink with a K value of 14 where the K value is the most ideal amount that must go through K - Fold Validation Validation of a total of 1,818 data.
Pembangunan Sistem Informasi Manajemen Aset pada PT. Kraft Ultrajaya Indonesia Mukti Kinani; Yulison Herry Chrisnanto; Irma Santikarama
Prosiding SISFOTEK Vol 4 No 1 (2020): Vol 4 No 1 (2020): SISFOTEK 2020
Publisher : Ikatan Ahli Informatika Indonesia

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Abstract

Asset management information systems are widely used by private and government companies because they can help companies and governments in managing assets that aim to manage, maintain, and supervise important assets. In this study, researchers tried to implement a system to manage asset management at PT. Kraft Ultrajaya Indonesia because there are still problems where asset management that does not have inaccurate information about the assets owned causes the managers not to have specific references used in determining needs in the procurement process. In addition, there is an inadequate monitoring process for the realization of assets received that are not in accordance with the purchase order form, resulting in a lack of information on the status of realization of assets received. In other cases there is also insufficient depreciation of asset maintenance information, resulting in inconsistencies in data and information regarding the condition of assets that are inconsistent with the condition of assets that are depreciated in the field, which may result in uncontrolling of the available assets. From this problem, researchers tried to implement an asset management information system in order to make it easier to help access historical data that is easy in the process of asset data processing and asset control starting from the asset planning process to revenue so that it can help the company evaluate each asset owned and simplify the decision-making process, thereby minimizing the purchase of excess assets that can harm the company and can also control every asset management that exists in the company. In addition, the asset management information system that is implemented will provide information on asset status management, especially the depreciation of each asset, so as to know the quality status of existing assets and the condition of asset depreciation.
Sistem Segmentasi Program Talk Show Berdasarkan Media Sosial Twitter Menggunakan Metode K-Medoids Clustering Kharisma Jevi Shafira Sepyanto; Yulison Herry Chrisnanto; Fajri Rakhmat Umbara
Prosiding SISFOTEK Vol 4 No 1 (2020): Vol 4 No 1 (2020): SISFOTEK 2020
Publisher : Ikatan Ahli Informatika Indonesia

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Abstract

Innovations on a talk show on television can be a threat. Audience will be divided into groups so that it can make a downgrade rating program. Program ratings affect companies that will use advertising services. Television companies will go bankrupt. The biggest source of income is sales of advertising services. One way to overcome them can be analyzed in public opinion. The results of the analysis can provide information about the attractiveness of the community towards the program. But the analysis process takes a long time and can be done only by a competent person so another process is needed to get the results of the analysis that is fast and can be done by anyone. In this study using K-Medoids Clustering in the process of identifying public opinion. The clustering process known as unsupervised learning will be combined with the labeling process. The previous episode's tweet data will be labeled and then used to obtain the predicted labels from other cluster members. Labels consist of three types, namely 1) theme, 2) resource persons, and 3) programs. Before going through the clustering stage, the tweet data will go through the text preprocessing stage then transformed into a numeric form based on the appearance of the word. Transformation data will be clustered by calculating proximity using Cosine Similarity. Labels from the Medoids cluster will be used on unlabeled tweet data. The cluster results were tested using the Silhouette Coefficient method to get 0.19 results. However, this method successfully predicted public opinion and achieved an accuracy of 80%. Innovations on a talk show on television can be a threat. Audience will be divided into groups so that it can make a downgrade rating program. Program ratings affect companies that will use advertising services. Television companies will go bankrupt. The biggest source of income is sales of advertising services. One way to overcome them can be analyzed in public opinion. The results of the analysis can provide information about the attractiveness of the community towards the program. But the analysis process takes a long time and can be done only by a competent person so another process is needed to get the results of the analysis that is fast and can be done by anyone. In this study using K-Medoids Clustering in the process of identifying public opinion. The clustering process known as unsupervised learning will be combined with the labeling process. The previous episode's tweet data will be labeled and then used to obtain the predicted labels from other cluster members. Labels consist of three types, namely 1) theme, 2) resource persons, and 3) programs. Before going through the clustering stage, the tweet data will go through the text preprocessing stage then transformed into a numeric form based on the appearance of the word. Transformation data will be clustered by calculating proximity using Cosine Similarity. Labels from the Medoids cluster will be used on unlabeled tweet data. The cluster results were tested using the Silhouette Coefficient method to get 0.19 results. However, this method successfully predicted public opinion and achieved an accuracy of 80%.
KLASIFIKASI SENTIMEN PERGELARAN MOTOGP DI INDONESIA MENGGUNAKAN ALGORITMA CORRELATED NAÏVE BAYES CLASIFIER RIDWAN INDRANSYAH; Yulison Herry Chrisnanto; Puspita Nurul Sabrina
INFOTECH journal Vol. 8 No. 2 (2022)
Publisher : Universitas Majalengka

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31949/infotech.v8i2.3103

Abstract

Knowing the public's sentiment towards the international MotoGP event which has been held in Indonesia in 2022 is very necessary because the role of the community is very influential in the implementation and public interest in visiting an international event is still few and difficult because the information is still limited. Tweets, comments, reviews, and opinions of people using social media play an important role in determining whether a particular population is satisfied with products, performances, and services. The method used in this study is the Correlated Naïve Bayes Classifier (CNBC). The Correlated Naive Bayes Classifier (CNBC) method recalculates the correlation value for each attribute of the dataset to that class. There are several processes carried out in this study including data acquisition, data labeling, data preprocessing, feature extraction, classifying data using the Correlated Naive Bayes Classifier (CNBC) method, visualizing data, and finally evaluating the results. This study resulted in an accuracy of 82%.
KLASIFIKASI PENENTUAN KELAYAKAN PINJAMAN KOPERASI DENGAN ALGORITMA CART MENGGUNAKAN ALGORITMA ADABOOST Muhammad Rendy Raihan; Yulison Herry Chrisnanto; Ade Kania Ningsih
INFOTECH journal Vol. 8 No. 2 (2022)
Publisher : Universitas Majalengka

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31949/infotech.v8i2.3247

Abstract

According to the Cooperative Bureau, cooperatives became a mainstay for the lower middle class to revive and stabilize their respective economies when the Covid-19 Pandemic broke out in Indonesia. Through savings and loan cooperatives, people can provide loans to cooperatives. In this case, cooperatives provide money lending services to their members, and certain conditions apply to determine which loans are eligible. In connection with this, the officer will analyze the loan by filling out a loan application form accompanied by certain requirements in each loan application. In a mechanism that is not simple, problems often arise when eligibility decisions are not appropriate, namely bad credit. This research aims to solve the problem by designing a data mining application with a function to determine the feasibility of giving loans to customers. The method used is the CART algorithm method and uses the Adaboost algorithm. The results of the application of the CART algorithm method optimized with Adaboost turned out to be able to classify the eligibility of cooperative lending well, simplify the mechanism in credit analysis activities and be able to provide accurate eligibility status, which is guaranteed by the accuracy results of CART and Adaboost.
KLASIFIKASI KALIMAT PADA BERITA OLAHRAGA SECARA OTOMATIS MENGGUNAKAN METODE ARTIFICIAL NEURAL NETWORK Asep Saepul Ridwan; Yulison H Chrisnanto; Ridwan Ilyas
J-Icon : Jurnal Komputer dan Informatika Vol 9 No 1 (2021): Maret 2021
Publisher : Universitas Nusa Cendana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35508/jicon.v9i1.3708

Abstract

Sports news is of great interest to today's society. This is because sports have grown into entertainment. There is a lot of sports news today that covers a wide range of sports, from branches that use the ball as objects for games like football, to sports in automotive race like formula 1. Beyond that, the substance of the sports news itself is as diverse as the news of managerial from a sports club, results matches, player injuries, et cetera. Surely such a thing would be difficult. The network wants one in the field of discussion in the news. Overlap data occurs in the sports news document because it mixes one sentence data with the other. Some of the content in the sports news is about managerial, players, schedules, previews, reviews, standings, statistics, champions, etc. Becomes a problem when the reader wants a topic on the news that focuses on one particular discussion. This study has built a book on sentence classification meaning Artificial Neural Network (ANN) with a method of learning Backpropagation. The feature used is the frequency of the occurrence of a term in the corresponding sentence and the calculating result of a distributed term. The testing of our proposed methods shows an accuracy of 99% to best results on training data and 57% on test data.
Model Sistem Penelurusan Alumni Institusi Pendidikan Tinggi Berbasis Teknologi Informasi Yulison Herry Chrisnanto; Wawan Setiawan; Puspo Dewi Dirgantari
Jurnal Ilmiah Matrik Vol 22 No 2 (2020): Jurnal Ilmiah Matrik
Publisher : Direktorat Riset dan Pengabdian Pada Masyarakat (DRPM) Universitas Bina Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33557/jurnalmatrik.v22i2.932

Abstract

Abstrak: Pemenuhan kebutuhan tenaga terampil yang siap pakai di dunia kerja akan dipengaruhi oleh kemampuan institusi pendidikan tinggi (IPT) dalam mengelola proses untuk menghasilkan lulusan yang produktif, sehingga upaya konstruktif perlu dibangun dalam mempersiapkan mutu lulusan yang dapat diterima di pasar kerja. Kebutuhan yang diperlukan oleh pasar tenaga kerja dapat diketahui oleh IPT sudah barang tentu dipengaruhi oleh informasi yang didapatkan dari para lulusan yang telah menjadi alumni. Dalam hal ini penelusuran alumni menjadi aspek yang penting bagi setiap IPT, di mana melalui penelusuran alumni ini dapat diperoleh berbagai informasi terkait produktivitas alumni pada dunia kerja. Penelitian ini melakukan kajian terhadap peranan teknologi informasi yang mampu memberikan dukungan bagi terbentuknya sistem penelusuran alumni. Pada penelitian ini diawali dengan berbagai kegiatan observasi terhadap dokumen-dokumen utama dan kondisi eksisting pada IPT, khususnya di Universitas Jenderal Achmad Yani (UNJANI) dan mengusulkan sebuah model system penelusuran alumni di institusi pendidikan tinggi (IPT) dengan berbasis teknologi informasi. Kata kunci: Model, Penelusuran almuni, Teknologi Informasi, institusi pendidikan tinggi
Perbandingan Improved K-Nearest Neighbour Dengan K-Nearest Neighbour Pada Analisis Sentimen Moda Raya Terpadu Jakarta Fahmy Akhmad Firdaus; Yulison Herry Chrisnanto; Puspita Nurul Sabrina
IJESPG (International Journal of Engineering, Economic, Social Politic and Government) Vol. 1 No. 3 (2023)
Publisher : IJESPG (International Journal of Engineering, Economic, Social Politic and Government)

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Abstract

ABSTRAK K-Nearest Neighbour merupakan algoritma klasifikasi yang dikenal sebagai metode berbasis jarak. Improved K-Nearest Neighbour merupakan perkembangan dari K-Nearest Neighbour yang memiliki perbedaan pada bobot nilai k yang memiliki nilai tetap Permasalahan dalam penelitian ini adalah bagaimana akurasi metode iknn dibandingan pada analisis sentimen Moda Raya Terpadu Jakarta (MRT). MRT Jakarta merupakan sebuah transportasi umum yang menggunakan listrik di Jakarta yang diharapkan dapat mengurangi angka kemacetan di daerah Jakarta. Pengoprasian MRT yang sudah secara resmi banyak menimbulkan respon dari masyarakat, baik itu respon yang positif, negatif, maupun netral. Untuk mengetahui hal tersebut, analisis sentiment dapat digunakan untuk mengklasifikasikan sebuah kalimat. Hasil penelitian dengan eksperimen dataset yang tidak balance dan yang balance di setiap kelasnya, eksperimen nilai K dan beberapoa splitting data menunjukkan bahwa peningkatan akurasi metode Improved K-Nearest Neighbour terhadap K-Nearest Neighbour pada kasus analisis sentiment moda raya terpadu tidak signifikan, dengan akurasi 77.24%, precission sebesar 0.77, Recall sebesar 0.77, dan F1 Score sebesar 0.77. Sedangkan metode ­K-Nearest Neighbour memiliki akurasi sebesar 76.12%, dengan precission sebesar 0.76, Recall sebesar 0.76, dan F1 Score sebesar 0.76. Kata kunci: Improved K-Nearest Neighbour. K-Nearest Neighbour, MRT Jakarta ABSTRACT K-Nearest Neighbor is a classification algorithm known as the distance-based method. Improved K-Nearest Neighbor is a development of K-Nearest Neighbor which has a difference in the weight of the value of k which has a fixed value. The problem in this research is how the accuracy of the Improved K-Nearest Neighbor method is compared to the sentiment analysis of the Jakarta Integrated Raya Mode (MRT). MRT Jakarta is a public transportation that uses electricity in Jakarta which is expected to reduce congestion in the Jakarta area. The operation of the MRT which has officially elicited many responses from the public, be it positive, negative or neutral responses. To know this, sentiment analysis can be used to classify a sentence. The results of the research with unbalanced and balanced dataset experiments in each class, experiments on K values and some data splitting show that the increase in accuracy of the Improved K-Nearest Neighbor method against K-Nearest Neighbor in the case of integrated modal sentiment analysis is not significant, with an accuracy of 77.24 %, precision of 0.77, Recall of 0.77, and F1 Score of 0.77. While the K-Nearest Neighbor method has an accuracy of 76.12%, with a precision of 0.76, Recall of 0.76, and F1 Score of 0.76.
Klasifikasi Data Kesehatan Mental di Industri Teknologi Menggunakan Algoritma Random Forest Emia Rosta Br. Sebayang; Yulison Herry Chrisnanto; Melina Melina
IJESPG (International Journal of Engineering, Economic, Social Politic and Government) Vol. 1 No. 3 (2023)
Publisher : IJESPG (International Journal of Engineering, Economic, Social Politic and Government)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26638/ijespg.v1i3.57

Abstract

Abstract : Mental health is an integral part of human well-being. Mental health disorders can affect individuals in various aspects of life. Work pressure, heavy workload, and an unhealthy lifestyle can be the main causes of mental health disorders in the workplace, such as industrial technology. Employees' mental health problems in the workplace often do not receive enough attention because they cannot be seen physically. Mental health has a significant impact on the performance that will be shown by employees in contributing to the company, it requires the company's prudence and sensitivity in observing and understanding the mental health conditions of employees. In this study, the Open Source Mental Illness (OSMI) survey data was classified using the Random Forest algorithm with the ensemble method, as well as the bootstrap tree method to improve the performance of the Random Forest algorithm in determining the accuracy of mental health data. The Random Forest algorithm is an ensemble learning method that combines several decision trees to improve prediction accuracy. Classification is carried out using a bootstrap tree which takes training data to train a model or ensemble so that it can take patterns and relations from the data to carry out classification, the Random Forest algorithm is an ensemble learning method that combines several decision trees for research with 80% training data and 20 test data %. The results of this study indicate a fairly good level of accuracy, which is 84%, so that it can make an important contribution in understanding the level of mental health disorders experienced by technology industry employees. The expected results of this research can improve the quality of life and productivity of employees at work.
Co-Authors Adam, Marcellino Ade Kania Ningsih Ade Kania Ningsih Ade Kania Ningsih, Ade Kania Aditya Prakasa Adryansyah Adryansyah Agung Wahana Agus Komarudin Andhika Karulyana Febrian Asep Id Hadianna Asep Saepul Ridwan Ashaury, Herdi Asri Maspupah Azzahra, Cynthia Nur Bania Amburika Benedictus Benny Sihotang Cahyaningrum, Amellia Fahezha Cecep M Zakariya Darmawan, Raja Dewi, Liony Puspita Didik Garbian Nugroho Drl, Indra Raja Eina, Muhammad Fikri Eka Rahmawati Emia Rosta Br. Sebayang Fadilah, Rifal Fahmy Akhmad Firdaus Faiza Renaldi, Faiza Fajar Tresnawiguna Fajri Rakhmat Umbara Fajri Rakhmat Umbara Farhan Naufal Febry Ramadhan Fitaloka, Intan Fuji Astari, Dhea Gerliandeva, Alfin Gita Mahesa Gunawan Abdillah Gunawan Abdillah Gunawan Abdillah Gunawan Abdillah, Gunawan Gunawan Abdullah Gunawan Gunawan Hadiana, Asep Id Haikal, Ahmed Hanafi, Willy Hanief Kuswanto, Muhammad Rafi Hendro Pudjiantoro, Tacbir Herdi Ashaury Herlina Napitupulu Herlinda Padillah Ibadirachman, Rifqi Karunia Id Hadiana , Asep Irma Santikarama Joko Irawan Julian Evan Chrisnanto Kamal, Angga Mochamad Kania Ningsih, Ade Kasyidi, Fatan Kharisma Jevi Shafira Sepyanto Kholidah Syaidah Kukuh Yulion Setia Prakoso Kusumaningtyas, Valentina Adimurti Luthfia Oktasari Mahendra, Lucky Syahroni Melina Melina Melina Melina Melina, Melina Mubarak, Muhammad Munzir Rizkya Muhamad Afnan, Zikri Muhammad Rendy Raihan Mukti Kinani Mulianti, Adhani Musa Asyari Hidayat Jati Nabilla, Ulya Naufal, Farhan Nida Ulhasanah Norizan Mohamed Permana, Hary Permatasari, Nissa Aulia Prawira, Angga Puspita Nurul Sabrina Puspita Nurul Sabrina Puspita Nurul Sabrina Puspita Nurul Sabrina Puspita Nurul Sabrina Puspita Nurul Sabrina, Puspita Nurul Puspo Dewi Dirgantari Putri Alifianti Wiyono, Tiara Putri Eka Prakasawati Raflialdy Raksanagara Rahandanu Rachmat Raja Darmawan Razaki, Adam Rd Muhammad Alfajri Reza Noviandi Rezki Yuniarti Ridwan Ilyas RIDWAN INDRANSYAH Riyadi, Saiful Faris Rizal Dwiwahyu Pribadi Santikarama, Irma Santoso, Enrico Budi Sepyanto, Kharisma Jevi Shafira Siska Vadilah Sukono . Sumantri, Fithra Aditya Taufiq Akbar Herawan Teguh Munawar Ahmad Tiara Rahmawati Umbara, Fajri Rakhmat Valentina Adimurti Kusumaningtyas Wahyu Pratama, Raka Wawan Setiawan Widinastia, Audila Gumanty Widiyantoro, Widiyantoro Wildah Fatma Lestari Wina Witanti Wisnu Uriawan, Wisnu Yosia Oktavian Pailan Zizilia, Regitha