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Talk Show Segmentation System Based on Twitter Using K-Medoids Clustering Algorithm Kharisma Jevi Shafira Sepyanto; Yulison Herry Chrisnanto; Fajri Rakhmat Umbara
Jurnal Pendidikan Teknologi Kejuruan Vol 3 No 3 (2020): Regular Issue
Publisher : Universitas Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24036/jptk.v3i3.15123

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. 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%.

Sistem Segmentasi Keluhan Pelanggan di Perumda Air Minum Tirta Raharja Cimahi Menggunakan Metode K-Medoids Febry Ramadhan; Yulison Herry Chrisnanto; Ade Kania Ningsih
Informatics and Digital Expert (INDEX) Vol. 3 No. 1 (2021): INDEX, MEI 2021
Publisher : LPPM Universitas Perjuangan Tasikmalaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36423/index.v3i1.674

Perumda Air Minum Tirta Raharja Cimahi merupakan sebuah perusahaan yang bergerak di bidang jasa pelayanan pengelolaan air minum dan pengelolaan sarana air limbah di kabupaten Bandung Barat, untuk meningkatkan kesejahteraan masyarakat yang mencakup aspek ekonomi, sosial, kesehatan dan pelayanan umum, namun dalam prosesnya tentu saja perusahaan banyak menerima keluhan dari konsumen yang berasal dari berbagai golongan pelanggan dengan jenis keluhan yang berbeda â€“ beda, dari keluhan â€“ keluhan tersebut tentu ada kecenderungan jenis keluhan yang ada pada suatu wilayah tertentu. Masalah yang ada meliputi tidak mengalirnya air, air yang berbau, dan lain sebagainya. Pendekatan yang digunakan untuk menyelesaikan masalah pada penelitian ini adalah menggunakan K-Medoids clustering. K-Medoids adalah teknik partisi yang berfungsi untuk mengelompokkan data set dari n objek kedalam kelompok k yang dikenal apriori. Dibandingkan dengan K-Means, K-Medoids lebih kuat untuk mengatasi kebisingan (noise) dan pencilan (outlier) karena meminimalkan sejumlah dissimiliarities berpasangan, bukan jumlah kuadrat jarak Euclidean.

PENGUKURAN TINGKAT KEMATANGAN TEKNOLOGI INFORMASI BERBASIS ITIL V.3 DI UNIVERSITAS JENDERAL ACHMAD YANI Rizal Dwiwahyu Pribadi; Yulison Herry Chrisnanto; Asep Id Hadiana; Wina Witanti
Jurnal Ilmiah Teknologi Infomasi Terapan Vol. 4 No. 1 (2017)
Publisher : Universitas Widyatama

[Id]Penggunaan Teknologi Informasi (TI) dalam perguruan tinggi sangatlah bermanfaat jika diterapkan dengan tujuan, visi, dan misi organisasi. Unjani (Universitas Jenderal Achmad Yani) merupakan perguruan tinggi yang telah menerapkan teknologi informasi dalam proses operasionalnya yang disesuaikan dengan tujuan, visi, dan misi organisasi. Dalam pencapaian visi organisasi maka penggunaan TI dalam organisasi harus selalu diawasi sehingga layanan yang diberikan kepada pengguna dapat maksimal. Pengukuran teknologi informasi dan komunikasi dilakukan menggunakan ITIL V.3, alasannya adalah metode tersebut lebih mudah digunakan karena sifatnya yang merekomendasikan sehingga organisasi yang telah mengimplementasikan ITIL V.3 hampir berbeda antara satu dengan yang lain. Tujuan dan hasil penelitian tersebut adalah untuk mengetahui tingkat kematangan TI yang dimiliki oleh Unjani sehingga Unjani dapat melakukan perbaikan untuk mencapai level kematangan optimal sesuai dengan standar ITIL.?[En]The use of Information Technology (IT) in universities is very useful if applied to the goals, vision, and mission of the organization. Unjani is a college that has applied information technology in its operational process that is aligned to the purpose, vision, and mission of organization. In achieving the vision of the organization then the use of IT in the organization should always be supervised so that services provided to users can be maximized. Measurement of information and communication technology is done using ITIL V.3, the reason is that it is easier to use because of its recommendation so that organizations that have implemented ITIL V.3 are almost different from each other. The purpose and result of this research is to know the level of IT maturity owned by Unjani so that Unjani can make improvements to achieve optimal maturity level according to ITIL standard.

The uses of educational data mining in academic performance analysis at higher education institutions (case study at UNJANI) Yulison Herry Chrisnanto; Gunawan Abdullah
Matrix : Jurnal Manajemen Teknologi dan Informatika Vol 11 No 1 (2021): MATRIX - Jurnal Manajemen Teknologi dan Informatika
Publisher : Unit Publikasi Ilmiah, P3M Politeknik Negeri Bali

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31940/matrix.v11i1.2330

Education is an important thing in a person's life, because by having adequate education, one's life will be better. Education can be obtained formally through formal institutions that constructively provide a person's abilities academically. This study aims to determine student performance in terms of academic and non-academic domains at a certain time during their education using techniques in data mining (DM) which are directed towards academic data analysis. Academic performance is delivered through the Educational Data Mining (EDM) integrated data mining model, in which the techniques used include classification (ID3, SVM), clustering (k-Means, k-Medoids), association rules (Apriori) and anomaly detection (DBSCAN). The data set used is academic data in the form of study results over a certain period of time. The results of EDM can be used for analysis related to academic performance which can be used for strategic decision making in aca-demic management at higher education institutions. The results of this study indicate that the use of several techniques in data mining together can maximize the ability to analyze academic performance with the same data source and produce different analysis patterns.

Sistem Pengelolaan Persediaan Berdasarkan Pola Hubungan Antar Produk Buah Olahan Menggunakan Association Rule Mining Siska Vadilah; Yulison Herry Chrisnanto; Puspita Nurul Sabrina
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 (568.552 KB)

association rule is analogous to a shopping basket. From the shopping basket the supermarket visitors will be able to know, what items are often bought together and which items are not. CV. Anugrah Paris Van Java is a company engaged in the processing of processed fruits such as ready-to-drink juice, concentrate and syrup. Customer's buying patterns are influenced by product inventory. The process of managing product inventory is managed manually so that there is a buildup of less desirable products. A frequent case is the lack of processed fruit products due to a lack of calculation from the purchase of raw materials. Based on these problems, a system for determining the pattern of relationships between processed fruit products was built as new knowledge to help the factory in the supply of processed fruit products. The method used in the system to be built is using the Association Rule Mining method with a priori algorithm. Based on research results, this system can display inventory recommendations based on analysis using association rule mining.

Sistem Segmentasi Keluhan Air Bersih di PT. Suryacipta Swadaya Menggunakan K-Medoids Clustering Reza Noviandi; Yulison Herry Chrisnanto; Herdi Ashaury
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 (341.728 KB)

Suryacipta Swadaya is a company that manages industrial estates. PT. Suryacipta Swadaya manages water in the industrial area to be distributed to 73 companies in the industrial area. Water in the industrial area sometimes has problems that companies complain about. There are 7 types of complaints with an average of complaints from 2017 to 2019, which is 8929 complaints per year. Water complaints for each company vary in the amount of distribution, however the water needs of each company are different even for the same company that has a diversity of water needs each year. Based on this, PT. Suryacipta Swadaya has difficulty mapping the water complaints group based on company needs, so that it is effective in handling water complaints in the industrial area. In this study the grouping of water complaints will be assisted using the K-Medoids method in the process of grouping water complaints so that the handling process can be handled immediately.

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)

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

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (408.793 KB)

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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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

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (718.428 KB)

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%.

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