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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 Fuzzy C-Means Pada Klasterisasi Penerima Bantuan Pangan Non Tunai Sola Huddin; Elin Haerani; Jasril Jasril; Lola Oktavia
KLIK: Kajian Ilmiah Informatika dan Komputer Vol. 4 No. 1 (2023): Agustus 2023
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/klik.v4i1.988

Abstract

One of the social assistance programs routinely provided by the government to Beneficiary Families (KPM) to overcome poverty problems in Indonesia at this time is Non-Cash Food Assistance (BPNT). The Pekanbaru City Social Service itself in distributing BPNT still experiences obstacles, such as the provision of assistance that is less targeted due to the absence of a system that is able to determine the recipient of aid appropriately. This research applies the Fuzzy C-Means Clustering method to analyze KPM data using MATLAB tools. This algorithm allows overlap between data groups and classifies KPM based on their characteristic patterns. This algorithm takes into account the membership level of each data in each group, thus providing more flexible results and not categorizing data rigidly. The results of the application of the FCM Clustering method in this study form two clusters, where the first cluster contains 331 data while in the second cluster there are 351 data. Testing the results of FCM clustering conducted using the Silhouette Coefficient method produces an average coefficient value of 0.426653079. Based on the value of the test results that have been carried out, the FCM algorithm is considered capable of forming clusters on BPNT data
Analisis Sentimen Masyarakat Terhadap Kenaikan Biaya Haji Tahun 2023 Menggunakan Metode Naïve Bayes Classifier Hertati; Elin Haerani; Novriyanto; Fadhilah Syafria
KLIK: Kajian Ilmiah Informatika dan Komputer Vol. 4 No. 3 (2023): Desember 2023
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/klik.v4i3.1457

Abstract

The Indonesian government through a meeting of the Ministry of Religion and Commission IVIII of the DPR-RI agreed on the cost of organizing the Hajj pilgrimage (BPIH) i1444 iH/2023 iM, an average of IDR 90,050,637.26 per irregular pilgrimage. However, this policy gave rise to various public responses. The public's anger regarding the increase in Hajj fees in 2023 was found on the social media iTwitter. In this study, we conducted a sentiment classification analysis of Tweets to determine public opinion regarding the increase in Hajj costs in 2023 using the naïve Bayes classifier method because this method tends to be simple and easy to use. The data set used was 3000 tweets with a total of 1866 positive data, 415 negative data. This research resulted in an accuracy value of 81.46% in the 70:30 data division, in the 80:20 data division, namely 80.74% and in the data division. 90:10 which is 79.04. In this research, there were more positive responses from the public, this proves that the increase in Hajj costs in 2023 can be accepted by the public. The highest accuracy in this study was 81.46% with a 70:30 data split. It is recommended that further research use other algorithms to see a comparison of the results of different algorithms in classifying public sentiment regarding the increase in the cost of Hajj in 2023.
Analisis Sentimen Tanggapan Masyarakat Terhadap Kenaikan Biaya Haji Tahun 2023 Menggunakan Metode K- Nearest Neighbor (KNN) Hafsyah; Elin Haerani; Novriyanto; Fadhilah Syafria
KLIK: Kajian Ilmiah Informatika dan Komputer Vol. 4 No. 3 (2023): Desember 2023
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/klik.v4i3.1471

Abstract

The Indonesian government implemented a policy of increasing the cost of Hajj in 2023, but the policy has attracted many positive and negative comments among the public. Public comments are taken from the social media network Twitter, because it contains a lot of information so that it attracts the interest of most people. With the increase in Hajj costs in 2023, it is necessary to conduct sentiment analysis. This study uses  the K-Neearest Neighbor method  because it is easy to apply and the data used are divided into two classes, positive and negative. The results of research on the application of  the K-Nearest Neighbor method in  sentiment analysis of the increase in Hajj costs in 2023 using 3,000 data taken from Twitter comments. The tweet data  used, there were 1866 positive comments and 415 negative comments and the total net data of 2281, judging from the amount of positive data compared to negative  data, obtained an accuracy value of 81.17% in 70:30 data sharing, 79.87% in 80:20 data sharing, 77.73% in 90:10 data sharing. Meanwhile, the highest accuracy value was 81.17% with  82.48% precision, 97.67% recall, F1- Score 89.43%.  In this study, there were more positive responses, this proves that the increase in Hajj costs in 2023 using  the K-Nearest Neighbor (KNN)  method can be accepted by the community
Pengukuran Tingkat Layanan Helpdesk Menggunakan COBIT 5 Febby Kurniawan; Novriyanto; Elin Haerani; Lola Oktavia
KLIK: Kajian Ilmiah Informatika dan Komputer Vol. 4 No. 3 (2023): Desember 2023
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/klik.v4i3.1474

Abstract

The Riau Provincial Information and Statistics Communication Service is a government agency tasked with formulating policies, conducting evaluations and reporting in the field of information and communication technology in various sectors of society. The Riau Provincial Information and Statistics Communication Agency has one of the services, namely a helpdesk to assist in handling problems related to the use of information technology. The helpdesk is one of the most important parts in the Riau Provincial Information and Statistics Communication Service because it is a liaison for each Regional Apparatus Organization (OPD), but the helpdesk at the Riau Provincial Information and Statistics Communication Service (Diskominfotik) does not yet have a benchmark that can be used to evaluate the performance of the helpdesk system. The purpose of this study is to determine the level or level of helpdesk services in optimizing information technology using the COBIT 5 framework and focusing on DSS03 Domain. This research was conducted by interviewing 8 respondents who were involved in the helpdesk and had 27 questions on the DSS03 domain. This research obtained the results of measuring the level of helpdesk service capability in Diskominfotik Riau Province  is at level 4, namely Predictable  Process where diskominfotik has run IT processes in accordance with established SOPs but needs to make continuous improvements in order to reach the target level to be achieved, which is at level 5 Optimizing Process
Penerapan Algoritma Naïve Bayes Classifier Dalam Klasifikasi Status Gizi Balita dengan Pengujian K-Fold Cross Validation Nurainun Nurainun; Elin Haerani; Fadhilah Syafria; Lola Oktavia
Journal of Computer System and Informatics (JoSYC) Vol 4 No 3 (2023): May 2023
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josyc.v4i3.3414

Abstract

Nutritional status is a condition related to nutrition that can be measured and is the result of a balance between nutritional needs in the body and nutritional intake from food. In Indonesia, there are still many nutritional problems such as malnutrition and other nutritional problems. This research will use the Naïve Bayes Classifier algorithm with K-Fold Cross Validation testing. The data used is data on the nutritional status of toddlers in August 2022 at the Rambah Samo I Health Center. Attributes in this study include Gender, Birth Weight, Birth Height, Age at Measurement, Weight, Height, ZS BB/U, BB/U, ZS TB/U, and TB/U. Determination of the nutritional status of toddlers in this study was based on the BB/TB index which consisted of 6 classes, namely severely wasted, wasted, normal, possible risk of overweight, overweight, and obese. From the research conducted, it was found that the Naïve Bayes Classifier algorithm with K-Fold Cross Validation can correctly classify the nutritional status of toddlers. From data processing using 10-Fold Cross Validation on the Naïve Bayes Classifier algorithm, it is known that the highest accuracy value is 82.94% in the 5th iteration, while the lowest accuracy value is 65.88% in 6th iteration. With an average overall accuracy value of 75.47%. Meanwhile, the average precision value obtained is 81.36% and the average recall value is 75.47%.
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 : Universitas 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.
Penerapan Algoritma Naïve Bayes Terhadap Klasifikasi Penerima Bantuan Program Keluarga Harapan (PKH) Amelia Irsyada; Elin Haerani; Muhammad Irsyad; Fitri Wulandari; Liza Afriyanti
Jurnal Sistem Komputer dan Informatika (JSON) Vol. 5 No. 2 (2023): Desember 2023
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/json.v5i2.7203

Abstract

Poverty in Indonesia is one of the complex social issues. As a manifestation of the government's concern about poverty in the country, various assistance programs have been established to target the impoverished population. One such program aimed at alleviating poverty in Indonesia is the Family Hope Program (Program Keluarga Harapan or PKH). PKH is a conditional cash transfer program provided to the impoverished community. The manual selection process for aid recipients is considered less than ideal, leading to issues of improper distribution. In this study, the Naïve Bayes algorithm is applied to classify PKH aid recipients in the Bungaraya Subdistrict, Siak Regency, as part of the government's efforts to tackle poverty. The dataset used consists of 560 records, including data on existing PKH aid recipients and potential recipients from various villages in the Bungaraya Subdistrict for the year 2022. The attributes considered in this research include age, income, number of dependents, dependents attending school, dependents with disabilities, housing status, floor type, and wall type. The highest accuracy obtained through calculations on Google Colab is 99% for an 80:20 ratio, while the accuracy obtained using RapidMiner is 94%.
Pemanfaatan Algoritma K-Means Dalam Menentukan Potensi Hasil Produksi Kelapa Sawit Ayu Sri Wahyuni; Elin Haerani; Elvia Budianita; Liza Afrianti
Jurnal Sistem Komputer dan Informatika (JSON) Vol. 5 No. 2 (2023): Desember 2023
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/json.v5i2.7226

Abstract

Mengingat pentingnya budidaya kelapa sawit saat ini dan masa depan, serta semakin meningkatnya permintaan minyak sawit oleh penduduk dunia, maka perlu dipikirkan upaya peningkatan kualitas dan kuantitas produksi minyak sawit secara tepat guna mencapai tujuan yang diinginkan dan dicapai. Berdasarkan data hasil produksi buah sawit PT Salim Ivomas Pratama Tbk terlihat di beberapa tempat produksi buahnya bervariasi. Potensi hasil buah kelapa sawit didasarkan pada luas panen, realisasi produksi dan tahun tanam nya Pengelasteran K-Means dapat membantu mengidentifikasi potensi kelapa sawit, dengan hasil yang bervariasi dari hari ke hari. Proses ini memungkinkan lokasi dengan pola produksi serupa, yang memfasilitasi keputusan manajemen dan strategi produksi. Pada penelitian ini, wilayah potensi penanaman buah-buahan dikelompokkan menggunakan algoritma K-Means. K-Means bertujuan untuk memfasilitasi pengelompokan blok dengan produksi buah tinggi dan rendah. Data yang digunakan ialah sebanyak 180 data selama 5 tahun terakhir yakni sejak tahun 2018 hingga tahun 2022, dengan atribut Blok Panen, Luas Area, Berat janjang, dan Realisasi produk atau jumlah. Penelitian ini menggunakan bantuan software Rapidminer dan Google Colab. Hasil dari penelitian ini di dapakan C1 (tertinggi) ialah 125 data Blok Panen dalam artian bahwa kelompok pertama termasuk kategori Hasil panen yang baik atau tinggi pada tahun 2018-2022, dan C0 (terendah) ialah 55 data Blok Panen dalam artian bahwa kelompok kedua termasuk kategori hasil panen rendah 2018-2022.
Co-Authors Amelia Irsyada Ayu Sri Wahyuni Elvia Budianita Fadhilah Syafria Febby Kurniawan Fitra Kurnia Fitri Wulandari Gusti, Siska Kurnia Hafsyah Hertati Iis Afrianty Jasril Jasril Liza Afrianti Liza Afriyanti Lola Oktavia Muhammad Irsyad 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 Siti Ramadhani Sola Huddin Tiara Ramayanti Yelfi Vitriani