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All Journal Jurnal Ilmiah Rekayasa dan Manajemen Sistem Informasi Jurnal Informatika Jurnal CoreIT Indonesian Journal of Artificial Intelligence and Data Mining Seminar Nasional Teknologi Informasi Komunikasi dan Industri Jurnal Sains dan Informatika Jurnal Informatika Universitas Pamulang Jurnal Teknologi Sistem Informasi dan Aplikasi Jurnal Nasional Komputasi dan Teknologi Informasi Krea-TIF: Jurnal Teknik Informatika JURIKOM (Jurnal Riset Komputer) Informatika : Jurnal Informatika, Manajemen dan Komputer JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Jutisi: Jurnal Ilmiah Teknik Informatika dan Sistem Informasi Progresif: Jurnal Ilmiah Komputer Jurnal Tekinkom (Teknik Informasi dan Komputer) JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) Journal of Computer System and Informatics (JoSYC) Jurnal Sistem Komputer dan Informatika (JSON) TIN: TERAPAN INFORMATIKA NUSANTARA Bulletin of Computer Science Research KLIK: Kajian Ilmiah Informatika dan Komputer Bulletin of Information Technology (BIT) Jurnal Informatika Teknologi dan Sains (Jinteks) Malcom: Indonesian Journal of Machine Learning and Computer Science Bulletin of Informatics and Data Science
Gusti, Siska Kurnia
Universitas Islam Negeri Sultan Syarif Kasim Riau
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Klasifikasi Penerima Bantuan Covid-19 Menggunakan Metode Weighted K-Nearest Neighbour Adi Mustofa; Okfalisa Okfalisa; Eka Pandu Cynthia; Yelfi Yelfi; Siska Kurnia Gusti
Jurnal Nasional Komputasi dan Teknologi Informasi (JNKTI) Vol 5, No 3 (2022): Juni 2022
Publisher : Program Studi Teknik Informatika, Fakultas Teknik. Universitas Serambi Mekkah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32672/jnkti.v5i3.4399

Abstract

Abstrak - Di awal tahun 2020 ini, World Health Organization (WHO) menerima laporan dari Cina mengenai kejadian infeksi berat yang belum diketahui penyebabnya. Kemudian WHO memberi nama virus ini sebagai Covid-19 pada 11 februari 2020. Covid-19 berdampak besar pada perekonomian global termasuk pada perekonomian Indonesia. Akibat penyebaran virus covid-19 banyak pelaku usaha mikro kecil (UMK) dan koperasi yang tidak bisa melakukan aktifitas usaha, Pelaku Industri Kecil yang tidak bisa melakukan aktifitas produksi, Jasa transportasi umum konvensional atau online dalam kota ,buruh, pekerja, atau tenaga harian lepas yang tidak memiliki pekerjaan, dan pekerja sektor formal yang dirumahkan atau korban Pemutusan Hubungan Kerja (PHK). Pemerintah daerah memberikan bantuan bagi masyarakat yang terdampak pandemi Covid-19. Dalam menentukan kelayakan penerima bantuan di kelurahan wonorejo masih menggunakan sistem manual, dimana penentuannya berdasarkan kriteria-kriteria dari hasil rekap data .Sehingga dalam jumlah data yang besar membutuhkan waktu yang relatif lama, serta ketelitian yang tinggi dalam menentukan kelayakan penerima bantuan. Untuk mengatasi permasalahan tersebut diperlukan sistem klasifikasi yang diharapkan dapat menentukan penerima bantuan. Metode yang dapat digunakan adalah Weighted KNN. Setelah dilakukan pengujian dengan Confusion Matrix dengan nilai k=5 didapatkan tingkat akurasi sebesar 87.69%.Kata Kunci: Bantuan, Covid-19, Klasifikasi, K-Neearest Neighbor, Weighted KNN. Abstract - In early 2020, the World Health Organization (WHO) received a report from China regarding the incidence of severe infections with unknown causes. Then on February 11, 2020 WHO named it as COVID-19. Covid-19 has had a major impact on the global economy, including the Indonesian economy. Due to the spread of the Covid-19 virus, many micro, small and medium enterprises and cooperatives are unable to carry out business activities, Small industry that unable to carry out production activities,public transportation service workers in the city both, laborer , and freelancer without a job, and laid-off formal sector workers. Local governments provide assistance to communities affected by the Covid-19 pandemic.  In determining the class of beneficiaries in Wonorejo District, the manual system is still used, where the determination is based on criteria from the results of the data recap. So that large amounts of data require a relatively long time, and high accuracy in determining the class of beneficiaries. To overcome these problem tersebut require classification system that expected to be able to determine the class of beneficiaries. The method that can be used is Weighted K-NN.The result of the Confusion Matrix method obtained an accuracy rate of 87.69%.Key Word:  Assistance, Covid-19, Classification, K-Neearest Neighbor, Weighted KNN
PREDIKSI DATA INDEKS HARGA KONSUMEN PROVINSI RIAU BERBASIS TIME SERIES DENGAN METODE DOUBLE EXPONENTIAL SMOOTHING Dina Septiawati; Siska Kurnia Gusti; Fadhilah Syafria; Yusra Yusra; Eka Pandu Cynthia
JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Vol 7, No 4 (2022)
Publisher : STKIP PGRI Tulungagung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29100/jipi.v7i4.3209

Abstract

Indeks Harga Konsumen merupakan indeks yang menghitung rata-rata perubahan harga barang dan jasa. Penelitian ini menggunakan data Indeks Harga Konsumen Provinsi Riau bulan Januari tahun 1999 sampai dengan bulan Desember tahun 2021 yang bersumber dari website resmi Badan Pusat Statistik Provinsi Riau. Penelitian ini bertujuan untuk memberikan gambaran tentang perkembangan indeks harga konsumen apakah mengalami kenaikan atau penurunan sehingga dapat dijadikan sebagai bahan evaluasi kebijakan yang akan diambil oleh pihak pemerintah, swasta, maupun pemegang otoritas moneter. Tahapan untuk prediksi dengan menggunakan metode double exponential smoothing yaitu menghitung nilai pemulusan tunggal (single smoothing), menghitung pemulusan ganda (double smoothing), menghitung nilai konstanta pemulusan, menghitung nilai kofisien trend, dan melalukan prediksi. Untuk melakukan pengujian prediksi maka dilakukan dengan cara perhitungan mean absolute percentage error. Berdasarkan perhitungan yang telah dilakukan, diperoleh hasil prediksi nilai indeks harga konsumen sebesar 105,17 dengan alpha 0,6 bernilai 3,132646%. Dapat disimpulkan bahwa metode double exponential smoothing mempunyai kemampuan yang baik dalam prediksi nilai indeks harga konsumen.
Klasifikasi Sentimen Masyarakat terhadap Kebijakan Vaksin Covid-19 pada Twitter dengan Imbalance Classes Menggunakan Naive Bayes Prima Yohana; Surya Agustian; Siska Kurnia Gusti
Seminar Nasional Teknologi Informasi Komunikasi dan Industri 2022: SNTIKI 14
Publisher : UIN Sultan Syarif Kasim Riau

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

Abstract

Penggunaan media sosial berkembang sangat pesat hingga sebuah informasi dalam bentuk apapun bisa viral (tersebar luas) dalam sekejap saja. Hal ini dikarenakan kebanyakan masyarakat telah memiliki telepon genggam baik dari usia anak-anak hingga dewasa. Masyarakat menggunakan media sosial twitter untuk berbagai kepentingan, antara lain memberi opini dan komentar. Terkait hal tersebut, dukungan dan penolakan juga banyak disampaikan dalam menanggapi program pemerintah untuk menangani pandemi COVID-19 (corona virus disease 2019) dengan mengadakan vaksinasi massal. Penelitian melakukan analisis dan klasifikasi adanya sentimen yang menggambarkan pandangan yang bersifat positif, negatif maupun netral masyarakat tentang covid-19 dengan menggunakan metode Naïve Bayes Classfier. Analisis dilakukan dengan mencari komposisi dataset yang relatif berimbang di antara kelas positif, negatif dan netral. Kombinasi tahapan teks preprocessing diselidiki untuk menghasilkan model NB yang memiliki performa terbaik dari data training, dan divalidasi menggunakan data development. Model final yang dipilih, menghasilkan akurasi 69,56% pada data development, kemudian diterapkan untuk menguji data testing yang belum pernah terlihat sebelumnya. Hasil akurasi yang diperoleh adalah 61% dengan F1-score sebesar 0,57. Pendekatan yang digunakan telah berhasil meningkatkan performa klasifikasi, karena berhasil mengidentifikasi kelas negatif dan positif dengan lebih baik, dibandingkan bila data digunakan apa adanya, tanpa melakukan balancing.
IMPLEMENTASI PENERIMA BANTUAN PANGAN NON TUNAI (BPNT) DENGAN MEGGUNAKAN METODE GENETIC MODIFIED K-NEAREST NEIGHBORI (GMKNN) Nurul Ikhsan; Siska Kurnia Gusti; Yusra; Fitri Insani; Fitri Wulandari
Jurnal Sains dan Informatika Vol. 8 No. 2 (2022): Jurnal Sains dan Informatika
Publisher : Teknik Informatika, Politeknik Negeri Tanah Laut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34128/jsi.v8i2.526

Abstract

Bantuan Pangan Non Tunai (BPNT) merupakan program pemerintah berupa bantuan pangan yang diberikan kepada Keluarga Penerima Manfaat (KPM) setiap bulannya, dimana kegiatan ini bertujuan untuk mengurangi beban pengeluaran dan mendukung nutrisi seimbang kepada KPM  BPNT menggunakan mekanisme akun elektronik untuk membeli pangan di e-Warung yang telah bekerjasama dengan Bank, namun pada pelaksanaan BPNT di Kota Pekanbaru, penerimaan bantuan masih dianggap kurang efisien, sehingga pada penelitian ini, penulis melakukan pembagian pada masyarakat yang bisa menerima bantuan dan tidak bisa menerima bantuan pangan non tunai dengan menggunakan metode MKNN. Kesimpulan yang didapatkan dari penelitian ini adalah Algen (Algoritma Genetika) bisa diterapkan dengan metode MKNN, dan mendapatkan hasil akurasi 86,89% dengan probabilitas crossover adalah 0,9 dan nilai probabilitas mutasi adalah 0,1 dengan nilai K yaitu 3.
DESAIN ARSITEKTUR DATA WAREHOUSE PADA DATA TRANSAKSI PENJUALAN ROTTE BAKERY Devi Julisca Sari; Siska Kurnia Gusti; Alwis Nazir; Elin Haerani; Fadhilah Syafria
Jurnal Tekinkom (Teknik Informasi dan Komputer) Vol 5 No 2 (2022)
Publisher : Politeknik Bisnis Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37600/tekinkom.v5i2.605

Abstract

The increasingly fierce competition between competitors requires companies to be able to compete and maintain their existence in order to continue to grow, for that utilizing information technology such as data warehouses will play a large enough role, because optimal data processing will produce quality information in supporting companies to take appropriate policies. as well as increasing the productivity and effectiveness of the company's performance. The application of the data warehouse can be started by making an architectural design that will be made, for that the researcher aims to provide recommendations for the design of the data warehouse architecture on the sales transaction data of Rotte Bakery by applying the Nine Steps Kimball method. The final result of this research is the application of the Nine Steps Kimball method and the integration of transaction data through the ETL process (extract, transform, load) successfully produces data stored in the data warehouse only the data that is needed and has been uninformed, so that data processing only takes a long time. shorter time in supporting appropriate policy making and achieving business strategies in order to be able to keep pace with the business competition
Klasifikasi Tingkat Keberhasilan Produksi Ayam Broiler di Riau Menggunakan Algoritma C4.5 Muhammad Rifaldo Al Magribi; Alwis Nazir; Siska Kurnia Gusti; Lestari Handayani; Iwan Iskandar
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.v10i1.5496

Abstract

Broiler chicken farming is one sector that contributes to playing an important role in causing an increase in the quality of life of the community, especially in fulfilling animal protein. Broiler chicken is a superior breed that has high meat productivity and a short reproductive cycle, thus encouraging the formation of partnerships between breeders and large companies. As the core, the company evaluates the success of breeders as seen from the performance index or IP value. The attributes that affect the IP value are depletion, average harvest weight, feed conversion ratio (FCR), and harvest age. The purpose of this research is to find out the attributes that most influence the success rate of broiler production in Riau and to get the accuracy value of the decision tree model using the C4.5 algorithm. This study used 952 livestock production data in Riau divided by a ratio of 80% training data and 20% test data. This test produces a decision tree in which the FCR attribute is the root node with a gain value of 0.45 and is the attribute that most influences the success rate of broiler chicken production in Riau. Evaluation using the confusion matrix produces an accuracy value of 97.11%, a precision of 98.89%, a recall of 98.16%.
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.
Klasifikasi Sentimen Transformasi dan Reformasi Sepak Bola Indonesia Pada Twitter Menggunakan Algoritma Bernoulli Naïve Bayes Destri Putri Yani; Siska Kurnia Gusti; Febi Yanto; Muhammad Affandes
Jurnal Sistem Komputer dan Informatika (JSON) Vol 4, No 3 (2023): Maret 2023
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/json.v4i3.5829

Abstract

Federation Internationale de Football Association (FIFA) carried out Transformations and Reformations to Indonesian Football with one of them Indonesia was chosen as the Host of the U-20 World Cup in 2023. The transformations and reformations carried out cause people to often provide opinions through social media Twitter. Opinions given by the public can be positive or negative. The research uses Text Mining to classify sentiment in 2 categories with the Bernoulli Naïve Bayes algorithm. This research aims to classify positive and negative sentiments and determine the level of accuracy value of the sentiment classification results of Indonesian Football Transformation and Reformation. The research stages carried out are data collection, text preprocessing, data labeling, TF-IDF weighting, Bernoulli Naïve Bayes classification, and evaluation. Based on the research results from 4907 data there is duplicate data and only uses 2125 data which is divided into 90% training data and 10% testing data, so as to get accuracy with a high category value of 88%. The classification results show that many tweets are positive sentiments.
Klasifikasi Tingkat Keberhasilan Produksi Ayam Broiler di Riau Menggunakan Algoritma K-Nearest Neighbor Beni Basuki; Alwis Nazir; Siska Kurnia Gusti; Lestari Handayani; Iwan Iskandar
Jurnal Sistem Komputer dan Informatika (JSON) Vol 4, No 3 (2023): Maret 2023
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/json.v4i3.5800

Abstract

Livestock is a crucial component of the Indonesian agriculture sector. One of the most widely practiced types of livestock farming is broiler chicken farming. The production of broiler chickens continues to increase due to the increasing consumption of broiler chickens. Presently, companies are facing an urgent requirement to support farmers, regardless of their level of experience, whether they are newly entering the sector or have been established for some time. Core companies encounter challenges in modeling the success rate of broiler chicken farmer production because of the vast quantity of data coming from collaborating farmers, which makes it arduous for the company to establish the success rate of broiler chicken production. Establishing the level of production success is very helpful in selecting the appropriate farmers to be guided, thus enabling accurate decision-making. A classification procedure utilizing data mining and K-Nearest Neighbor (KNN) algorithm is necessary to manage the growing volume of data. The study examined 927 livestock production data from Riau, where the data was divided into two sets, with 80% allocated for training and the remaining 20% for testing purposes. The findings of the confusion matrix analysis showed that the optimal result was achieved at k = 3, with an accuracy rate of 86.49%, precision of 75.00%, and recall of 70.21%.
Klasifikasi Sentimen Tragedi Kanjuruhan Pada Twitter Menggunakan Algoritma Naïve Bayes Iqbal Salim Thalib; Siska Kurnia Gusti; Febi Yanto; Muhammad Affandes
Jurnal Sistem Komputer dan Informatika (JSON) Vol 4, No 3 (2023): Maret 2023
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/json.v4i3.5852

Abstract

The Kanjuruhan Malang incident occurred on October 1 and resulted in 132 deaths, 96 serious injuries and 484 minor injuries. The cause of the riot occurred due to provocation between Arema Malang supporters and Persebaya Surabaya supporters who mentioned harsh words and other provocative actions that caused anger on both sides. Sentiment analysis of the Kanjuruhan tragedy using the Naive Bayes method was conducted through tweets taken through Twitter to understand the public's perception of the incident. The Naïve Bayes algorithm is performed for the sentiment classification of tweet data which is applied by processing the tweet text and classifying it into positive, negative, and neutral. In this study using data as much as 4843 data and carried out with tweet data that has been crawled resulting in 2,042 data. This research aims to classify sentiment and determine the level of accuracy in the Multinomial Naïve Bayes algorithm in the Kanjuruhan tragedy using a dataset in the form of tweets from twitter social media. The processed tweet data is divided into two types, namely 90% training data and 10% test data.  The results of this classification get a Naïve Bayes accuracy of 75% with a precission of 73%, recall of 75%, and f1-score value of 74%. The results of the tweet data used in this study can be concluded that the Naïve Bayes algorithm has a fairly good accuracy value.
Co-Authors Abdul Wahid Abdullah Abdullah Abdullah, Said Noor Abdussalam Al Masykur Adi Mustofa Al Rasyid, Nabila Alfaiza, Raihan Zia Alfin Hernandes Alwaliyanto Alwis Nazir Alwis Nazir Alwis Nazir Amelia, Felina Anggi Vasella Azhima, Mohd Baehaqi Beni Basuki Cut Lira Kabaatun Nisa Destri Putri Yani Devi Julisca Sari Dina Septiawati efni humairah Eka Pandu Cynthia Eka Pandu Cynthia Elin Haerani Elin Haerani Elin Haerani Elin Haerani Elvia Budianita Erni Rouza, Erni Fadhilah Syafria Faska, Ridho Mahardika Febi Yanto Fitri Insani Fitri Insani Fitri Wulandari Fitri, Anisa Gusti, Gogor Putra Hafi Puja Hamwar, Syahbudin Iis Afrianty Iis Afrianty Iqbal Salim Thalib Irsyad (Scopus ID: 57204261647), Muhammad Iwan Iskandar Jasril Jasril Jasril Jasril Khair, Nada Tsawaabul Kurniansyah, Juliandi Lestari Handayani M Wandi Dwi Wirawan Maemonah, Maemonah Morina Lisa Pura Muhammad Affandes Muhammad Fauzan Muhammad Irsyad Muhammad Khairy Dzaky Muhammad Rifaldo Al Magribi Nazir, Alwis Norhiza, Fitra Lestari Novriyanto Novriyanto Nurul Ikhsan Okfalisa Okfalisa Pizaini Pizaini Prima Yohana Rahmah Miya Juwita Raja Indra Ramoza Ramadhani, Astrid Risfi Ayu Sandika Robbi Nanda Robby Azhar Sardi, Hajra Satria Bumartaduri Sayyid Muhammad Habib Siti Ramadhani Siti Ramadhani Siti Ramadhani Surya Agustian Suwanto Sanjaya Syafira, Fadhilah Syafria, Fadhillah Syaputra, Muhammad Dwiky Umam, Isnaini Hadiyul Vusuvangat, Imam Wulandari, Fitri Yayuk Wulandari Yelfi Yelfi Yola, Melfa Yusra Yusra, - Yusra, Yusra