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All Journal JURNAL MEDIA INFORMATIKA BUDIDARMA JURIKOM (Jurnal Riset Komputer) Building of Informatics, Technology and Science JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) Journal of Computer System and Informatics (JoSYC) Jurnal Sistem Komputer dan Informatika (JSON) KLIK: Kajian Ilmiah Informatika dan Komputer
Elin Haerani
Universitas Islam Negeri Sultan Syarif Kasim Riau, Pekanbaru
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Industrial & Manufacturing Engineering

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Penerapan dan Implementasi Metode Certainty Factor Dalam Sistem Pakar Diagnosa Awal Gangguan Menstruasi PALM-COEIN Putri Azzahra; Elin Haerani
Building of Informatics, Technology and Science (BITS) Vol 4 No 2 (2022): September 2022
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v4i2.1805

Abstract

Menstrual disorders are changes that occur in the cycle, the amount of blood, and other changes related to the menstrual cycle. Menstrual disorders are often considered a common problem that can affect daily activities. Menstrual disorders that are ignored on an ongoing basis can cause various reproductive system disorders and are also an early sign of dangerous diseases. Many women ignore the menstrual disorders they experience because they are constrained in consulting a doctor both in terms of time, distance, and cost. To help overcome these obstacles, an expert system can be a solution. This expert system is made using the certainty factor method which consists of 43 symptoms with 7 types of disease. This system produces output in the form of a percentage of the type of possible disease experienced by the user and suggestions based on the symptoms selected by the user. The expert system built is expected to be a medium of information about menstrual disorders and can contribute to providing benefits to the community, especially women, to make an initial diagnosis of menstrual disorders experienced without being constrained in terms of time, distance, or cost
Pengelompokkan Penyakit Pasien Menggunakan Algoritma K-Means Rahayu Anggraini; Elin Haerani; Jasril Jasril; Iis Afrianty
JURIKOM (Jurnal Riset Komputer) Vol 9, No 6 (2022): Desember 2022
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v9i6.5145

Abstract

Health is one of the most important factors besides education and income. Everyone has the same human rights to get good health services. A government agency that functions to serve all people who need medical services in Indonesia, namely the puskesmas. Ujung Batu Health Center which is located in Ujung Batu sub-district, Rokan Hulu Regency as one of the government agencies. The Ujung Batu health center stores patient medical record data, only sorting out the disease. Therefore, the medical record data needs to be processed using clustering or grouping using the K-Means method. This algorithm partitions the data into clusters so that data with the same characteristics are grouped into the same cluster and data with different characteristics are grouped. into another cluster. The data used consisted of 3875 records and 5 attributes, namely Gender, Participant Type, Diagnosis, Return Status, Address. From the test using the K-means algorithm, the clustering results show that cluster 1 has 710 data while cluster 2 has 3165 data. The results of the study show that the use of 2 clusters is the best cluster with a Silhouette Coefficient value showing results with a SC value of 0.646.
Penerapan Metode Clustering Dalam Pengelompokan Kasus Perceraian Pada Pengadilan Agama di Kota Pekanbaru Menggunakan Algoritma K-Medoids Satria Bumartaduri; Siska Kurnia Gusti; Fadhilah Syafria; Elin Haerani; Siti Ramadhani
JURIKOM (Jurnal Riset Komputer) Vol 10, No 1 (2023): Februari 2023
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v10i1.5560

Abstract

Divorce is the breaking of a husband and wife relationship from a marriage. When a couple does not want to continue their marriage relationship, one of the factors causing divorce is that the husband and wife do not carry out their duties properly. Divorce cases also occur in the city of Pekanbaru and have increased from 2020 to 2022. In connection with this problem, researchers conducted research with the aim of classifying districts in Pekanbaru that have the most divorces. The method used in this study is K-Medoids Clustering, because this method can divide a dataset into several groups. The advantage of this method is that it can overcome the weaknesses of the K-Means algorithm which are sensitive to outliers. The tests used in this study use the RapidMiner tools and the Davies Bouldin Index to ensure cluster accuracy. Attributes used in this research are region/regency, age difference between spouses, plaintiff's and defendant's education, and reasons for divorce. The results of this study can be used as information for the government to reduce the divorce rate in the city of Pekanbaru so that appropriate programs can be developed for each sub-district in overcoming the divorce rate in Pekanbaru. From testing using the K-Medoids algorithm, the cluster results obtained showed that the highest divorce rate was in cluster 1 with 565 items, while cluster 2 had 395 items and cluster 3 had 288 items. The results of the study show that the use of 3 clusters is the best cluster with a DBI value of 0.884.
Analisis Sentimen Ulasan Aplikasi WeTV Untuk Peningkatan Layanan Menggunakan Metode K-Nearst Neighbor Nurkholimah Faridhotun; Elin Haerani; Reski Mai Candra
Journal of Information System Research (JOSH) Vol 4 No 3 (2023): April 2023
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v4i3.3349

Abstract

Online streaming applications are activities for watching movies that are very popular with the public, one of which is WeTV. WeTV is an online streaming that is used by the public as a medium of entertainment. The WeTV application has a rating of 4 out of 256 thousand reviews written by its users. The collection of reviews in the form of text can be collected and classified into negative responses, neutral responses, and negative responses. Positive responses are comments that are optimistic or supportive. Negative responses are comments on phrases or cases that do not support statements about related cases. Neutral responses are comments that are difficult to understand between negative or positive in nature to provide suggestions, sentences that have reviews from application users can be positive, negative and neutral, the data will go through a classification process using the K-Nearst Neighbor method. In this study, 12,000 reviews were used from the playstore. The research used the preprocessing stage, namely cleaning, case folding, tokenizing, normalization, stopword removal and steaming then to the TF-IDF stage and the final results will be tested with a confusion matrix using the Python programming language. The highest accuracy results from the testing process with a value of K = 3 in the dataset model 90% training data and 10% test data obtain an accuracy of 0.70%, precision 0.76%, recall 0.69%, f1-score 0.72% . Based on the results of the research that the K-Nearest Neighbor method is good in the process of identifying negative responses on WeTV.
Analisis Sentimen Ulasan Aplikasi Wetv Untuk Peningkatan Layanan Menggunakan Metode Support Vector Machine Rezky Abdillah; Elin Haerani; Reski Mai Candra
Journal of Information System Research (JOSH) Vol 4 No 3 (2023): April 2023
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v4i3.3353

Abstract

Wetv is an online streaming media that has been running since 2019. Wetv has many user reviews from various applications. The rating consists of positive, neutral and negative. The response is used to determine sentiment by using the support vector machine classification method. This study took 12,000 comments from the Google Play Store, this study used preprocessing namely, cleaning, case folding, tokenizing, normalization, stopword removal, and steaming, then to the TF-IDF stage and the final results were tested with a fusion matrix with the Python program, the score results highest from the acquisition test process with accuracy of 0.76%, precision of 0.77%, recall of 0.79%, and f1 score of 0.78, in a dataset of 90% training data and 10% test data. Based on the research results of the Support Vector Machine method which is known to be good in the process of requesting negative responses on WeTV.
Analisa Sentimen Ulasan Aplikasi Wetv Untuk Peningkatan Layanan Menggunakan Metode Naïve Bayes Novi Lestari; Elin Haerani; Reski Mai Candra
Journal of Information System Research (JOSH) Vol 4 No 3 (2023): April 2023
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v4i3.3355

Abstract

The most popular online streaming application is WeTV. WeTV is an internet-based streaming service that is used by the public as an entertainment medium. The WeTV application has been downloaded by up to 50,000 users. Application user ratings may affect the image of the application depending on the services provided by the application developer. Many positive, neutral and negative reactions have had a big impact on WeTV. Categorizing user ratings cannot be done manually because it is not easy with very large amounts of data. Therefore, the purpose of this research is to analyze the user rating of the WeTV application on the Goggle Playstore. In this study the processing steps consisted of cleaning, case convolution, tokenization, normalization, stopword and vape removal, after which it was continued with the TF-IDF step and the final result was a confused matrix using the Python programming language with Naive Bayes classifier. method in this research. Using 12,000 reviews found on Google Playstore. to generate positive, negative and neutral sentiments from Wetv application user comments in the play store. The test with the highest precision value of 0.64% with a -1 precision value of 0.58% in Class Recall gives a value of 0.89% in the 90%:10 balance model.
Analisis Sentimen Tanggapan Masyarakat Terhadap Calon Presiden 2024 Ridwan Kamil Menggunakan Metode Naive Bayes Classifier Neni Sari Putri Juana; Elin Haerani; Fadhilah Syafria; Elvia Budianita
Jurnal Sistem Komputer dan Informatika (JSON) Vol 4, No 4 (2023): Juni 2023
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/json.v4i4.6168

Abstract

Reaction to public facts about the election of the presidential candidate Ridwan Kamil, which will later be obtained, the data is taken from Twitter based on these problems, it is necessary to do sentiment analysis research. Based on the results of this study, the classification process for the Naïve Bayes Classifier has 3 scenarios for dividing training data and test data, namely with 90%:10% training data, the test data produces an accuary value of 85.43%, a recall value of 100.00%, and a precision of 85.33%. For training data 80%: 20% of the test data produces an accuracy value of 86.38%, a recall of 100.00% and a precision value of 86.38% and for data on the distribution of training data 70%: 30% of the test data produces an accuary value of 84.29 %, 100.00% recall and 84.29% precision. From the tweet data that has been used, there are 1262 positive comments and 242 negative comments. These results prove that the Naïve Bayes classifier is very good for conducting sentiment analysis on Twitter comments about the 2024 presidential candidate Ridwan Kamil. The naïve Bayes classifier process gets the highest accuracy value of 86.38% by dividing the training data 80%:20% test data.
Sistem Rekomendasi Tempat Wisata di Provinsi Riau dengan Metode Simple Additive Weighting (SAW) Risky Silitonga; Yelfi Vitriani; Elin Haerani; Fitra Kurnia
KLIK: Kajian Ilmiah Informatika dan Komputer Vol. 3 No. 6 (2023): Juni 2023
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/klik.v3i6.929

Abstract

Riau is a province that has quite good potential for new tourist attractions and tourist destinations that can be developed further. Because there are quite a lot of tourist attractions in Riau, it is difficult for the community to choose recommendations for tourist attractions that suit the wishes of the user. Therefore, the researcher created a recommendation system to assist the community in choosing the tourist attractions they want to visit. In developing this recommendation system, the Simple Additive Weighting method is used, where this method ranks alternative tourist destinations according to predetermined criteria values. The criteria offered by the system are distance, cost, facilities, time and accessibility. The system created can help people to choose the right tourist destination. Web-based built system. The recommendation system for tourist attractions in Riau Province with the Simple Additive Weighting method has succeeded in assisting users in selecting tourist attractions according to predetermined criteria values. Then the best recommendation for tourist attractions on behalf of Ulo Kasok tourist attractions is obtained with the final result value ( 88.80), Based on the UAT test, the results obtained were 83%, which means that the user really agreed
Sistem Rekomendasi Hotel Di Provinsi Riau Dengan Metode AHP dan SAW Nurul Ramadiah Madjid; Yelfi Vitriani; Elin Haerani; Fitra Kurnia
KLIK: Kajian Ilmiah Informatika dan Komputer Vol. 3 No. 6 (2023): Juni 2023
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/klik.v3i6.931

Abstract

Riau Province is one of the provinces that has been equipped with various recreational facilities, sports facilities and tours that are very interesting to visit. With the increasing number of facilities and tourist attractions that can be visited, the hotel is one place that is needed as a lodging facility. Hotels in Riau Province are also growing very rapidly. Riau provides so many choices of hotels scattered in various locations with various hotel classes, rental prices, facilities and services. The system is a set of components and combined elements, organized and collaborative components to achieve a certain goal, namely providing information. AHP is a decision support model that will describe complex multi-factor or multi-criteria problems into a hierarchy. The SAW method is known as the term weighted sum. The basic concept of the SAW method is to find the weighted sum of the performance ratings for each alternative on all attributes. The results obtained from this system are systems that are made into the good category by applying the AHP and SAW methods. The Hotel Recommendation System in Riau Province with the AHP and SAW Methods can help the Tourism Office and the community in selecting hotels. The Hotel Recommendation System using the AHP and SAW methods has been able to produce hotel recommendations in Riau Province according to needs and based on criteria determined by the user. Based on the Black Box testing, it shows that the system can run well with its functions, and with the UAT testing that has been carried out, a result of 82% is obtained where the results fall into the strongly agree category
Penerapan Algoritma K-Medoids Pada Clustering Penerima Bantuan Pangan Non Tunai (BPNT) Tiara Ramayanti; Elin Haerani; Jasril Jasril; Lola Oktavia
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 7, No 3 (2023): Juli 2023
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v7i3.6475

Abstract

Bantuan Pangan Non Tunai (BPNT) is assistance distributed by the government to underprivileged communities to ease the financial burden that is increasingly burdening their lives. In a number of cases, it was found that the number of people who received BPNT was not properly targeted, so it was necessary to analyze the pattern of the characteristics of BPNT recipients so that the assistance was right on target. There are many criteria that must be considered to determine the people who are entitled to receive BPNT, so an appropriate algorithm is needed to determine the right cluster when analyzing characteristic patterns. This study applies the K-Medoids algorithm to classify BPNT data obtained from Firza Syahputra's research in 2020–2021, with a total of 732 attributes, so that the government can consider the factors that characterize beneficiaries. Perform tests using the Silhouette coefficient, which is useful for maximizing clustering results. The clustering result is three clusters, and the silhouette coefficient is 0.4439221599010089. The results of the analysis show that clustering performed using the K-Medoids algorithm can assume that clusters are grouped according to grouping: cluster 0 is eligible to receive BPNT, cluster 1 is considered, and cluster 2 is not eligible to receive BPNT.
Co-Authors Elvia Budianita Fadhilah Syafria Febby Kurniawan Fitra Kurnia Hafsyah Hertati Iis Afrianty Jasril Jasril Lola Oktavia Neni Sari Putri Juana Novi Lestari Novriyanto Novriyanto Nurainun Nurainun Nurkholimah Faridhotun Nurul Ramadiah Madjid Putri Azzahra Rahayu Anggraini Reski Mai Candra Rezky Abdillah Risky Silitonga Satria Bumartaduri Siska Kurnia Gusti Siti Ramadhani Sola Huddin Tiara Ramayanti Yelfi Vitriani