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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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Penerapan Metode K-Means Clustering untuk Pemetaan Pengelompokan Lahan Produksi Tandan Buah Segar Abdussalam Al Masykur; Siska Kurnia Gusti; Suwanto Sanjaya; Febi Yanto; Fadhilah Syafria
Jurnal Informatika Vol 10, No 1 (2023): April 2023
Publisher : LPPM Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/inf.v10i1.15621

Abstract

Di Perkebunan Sei Lukut, Desa Maredan Barat, Kecamatan Tualang, Kabupaten Siak, Provinsi Riau, PT. Surya Intisari Raya, sebuah perusahaan swasta, mengelola perkebunan kelapa sawit. Memiliki 4 bagian lahan kelapa sawit yang terdiri dari 216 blok dengan total sekitar 4.000 Ha. Blok kelapa sawit biasanya mencakup 20 hektar dan berisi 28.000 pohon kelapa sawit, dengan kapasitas produksi bulanan sebesar 57 ton. Pemetaan klaster produksi tandan buah segar berupaya membantu pelaku usaha memutuskan kebijakan apa yang akan diterapkan untuk meningkatkan akurasi dan produktivitas produksi minyak sawit. Metode K-Means merupakan komponen dari metode clustering, yang merupakan subset dari kelompok Unsupervised Learning dan digunakan untuk mempartisi data ke dalam berbagai kategori. Untuk mengelompokkan blok lahan berdasarkan delapan data variabel luas pokok, panjang panen, daun lepas, curah hujan, pupuk, tujuan, dan persentase keberhasilan, penelitian ini akan menerapkan Indeks Davies Bouldin dengan alat RapidMiner. Kesimpulan akhir dari penelitian ini adalah sebuah aplikasi yang dapat memetakan pengelompokan areal produksi tandan buah segar dengan menerapkan metode K-Means Clustering, dengan nilai Davies Bouldin Index terkecil sebesar 0,921 pada jumlah cluster 3 yang termasuk Cluster C1 (Produktivitas Sedang). Terdiri dari 96 blok tanah, Cluster C2 (Produktivitas Rendah) terdiri dari 41 blok tanah, dan Cluster C3 (Produktivitas Tinggi) terdiri dari 79 blok tanah.In Sei Lukut Estate, West Maredan Village, Tualang District, Siak District, Riau Province, PT. Surya Intisari Raya, a private business, administers oil palm plantations. It has 4 sections of oil palm land made up of 216 blocks totaling about 4,000 Ha. Blocks of oil palm typically cover 20 hectares and contain 28,000 palm trees, with a monthly output capacity of 57 tons. The mapping of the production clusters for fresh fruit bunches seeks to help the business decide what policies to implement to increase the accuracy and productivity of palm oil production. The K-Means method is a component of the clustering method, which is a subset of the Unsupervised Learning group and is used to partition data into various categories. In order to group land blocks based on the eight variable data areas of total principal, harvest length, loose leaf, rainfall, fertilizer, goal, and percentage of success, this study will apply the Davies Bouldin Index with RapidMiner tools. The final conclusion of this research is an application that can map the grouping of fresh fruit bunch production areas by applying the K-Means Clustering method, with the smallest Davies Bouldin Index value of 0.921 in the number of clusters 3 including Cluster C1 (Medium Productivity) consisting of 96 blocks land, Cluster C2 (Low Productivity) consists of 41 land blocks, and Cluster C3 (High Productivity) consists of 79 land blocks.
Implementasi Triple Exponential Smoothing dan Double Moving Average Untuk Peramalan Produksi Kernel Kelapa Sawit Risfi Ayu Sandika; Siska Kurnia Gusti; Lestari Handayani; Siti Ramadhani
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.v4i3.3359

Abstract

The production of palm kernel is a significant product for the company and plays a crucial role. Nevertheless, the stability of kernel production is not always consistent, and the quality of the kernel can be detrimental to the company. As consumer demands change over time, companies must anticipate every fluctuation in palm kernel production. Hence it is vital to figure the long run with a settlement prepare utilizing information mining utilizing information within the past. The Triple Exponential Smoothing and Double Moving Average methods, which are data mining methods for future forecasting, were used in this study. The aim of this research is to predict the yield of future oil palm kernel production using the Triple Exponential Smoothing and Double Moving Average methods and to determine the level of forecasting errors using the Mean Absolute Percentage Error (MAPE) method. The data for the last ten years, from January 2013 to December 2022, were used in this study. After testing the Triple Exponential Smoothing method with parameters α=0.2,β=0.γ=0.2, the error rate using MAPE was 9.48%, and the Double Moving Average method had an error rate of 11.2%. The MAPE results of the Triple Exponential Smoothing method are considered very good, while the MAPE results of the Double Moving Average method are categorized as good based on the range of MAPE values. This research is expected to provide information to related companies as a supporting reference in anticipating palm oil kernel production. The conclusion of the research is that the Triple Exponential Smoothing method with the test parameters is the best method for forecasting.
Penerapan Triple Exponential Smoothing dan Arima dalam Memprediksi Produksi Crude Palm Oil Anggi Vasella; Siska Kurnia Gusti; Lestari Handayani; Siti Ramadhani
Jurnal Informatika Universitas Pamulang Vol 8, No 2 (2023): JURNAL INFORMATIKA UNIVERSITAS PAMULANG
Publisher : Teknik Informatika Universitas Pamulang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32493/informatika.v8i2.30979

Abstract

Dalam bidang perkebunan sawit, PT.XYZ yang terletak di Provinsi Riau merupakan perusahaan yang menghasilkan salah satunya Crude Palm Oil (CPO). Diketahui bahwa dari 10 tahun terakhir produksi, harga jual yang cenderung tidak stabil berakibat terjadinya penimbunan stok. Maka dari itu, dilakukan peramalan jumlah produksi yang tepat agar masalah penimbunan stok dapat diatasi, sehingga penelitian ini bertujuan untuk melakukan prakiraan stok produksi CPO menggunakan perbandingan dua algoritma yaitu Triple Exponential Smoothing dan ARIMA. Peramalan melibatkan pengambilan data historis serta memprediksikannya untuk periode selanjutnya. Setelah dilakukan proses peramalan maka dilakukan pengujian tingkat kesalahan dalam peramalan memakai metode Mean Absolute Percatage Error (MAPE) untuk menunjukkan kisaran nilai kesalahan dalam perhitungan peramalan berdasarkan kesalahan terkecil. Output setelah dilakukan pengujian dengan metode TES mendapatkan tolak ukur αlpha=0,5, βeta=0,004, dan gamma γ=1,0 tingkat kesalahan diperoleh dengan menggunakan akurasi MAPE 10,1% dan 1,4% untuk model ARIMA. Pada output metode TES mendapatkan kategori MAPE dengan kemampuan peramalan baik dan sedangkan output metode ARIMA termasuk dalam kategori MAPE dengan kemampuan peramalan sangat baik sesuai penilaian rentang MAPE. Peran penelitian ini dibutuhkan agar memberikan informasi kepada perusahaan terkait sebagai referensi tambahan dalam peramalan produksi CPO. Hasil kajian metode terbaik yang dilakukan mendapatkan kesimpulan bahwa metode ARIMA dengan perhitungan kesalahan terkecil dari nilai MAPE.
PERAMALAN PENJUALAN GAS OKSIGEN MENGGUNAKAN ALGORITMA DOUBLE EXPONENTIAL SMOOTHING Cut Lira Kabaatun Nisa; Alwis Nazir; Siska Kurnia Gusti; Lestari Handayani; Suwanto Sanjaya
I N F O R M A T I K A Vol 15, No 1 (2023): MEI, 2023
Publisher : STMIK DUMAI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36723/juri.v15i1.521

Abstract

Perusahaan yang baik perlu melakukan pengembangan terhadap usaha yang dimiliki demi kepuasan relasi, salah satu usaha perusahaan dalam melakukan pengembangan dalam bisnis adalah melakukan peramalan, peramalan penjualan bertujuan dalam menentukan keputusan untuk masa yang akan datang. Produk gas oksigen merupakan salah satu jenis produk gas yang diproduksi dan di distribusikan dalam bentuk tabung, lonjakan kebutuhan gas oksigen pada masa pandemic covid-19 mengakibatkan angka kebutuhan gas oksigen meningkat sehingga kebutuhan akan produk tersebut tidak dapat terkendali dan mengakibatkan permintaan yang tidak dapat terpenuhi. Peramalan ini bertujuan untuk membantu perusahaan menentukan strategi dalam meramalkan kebutuhan stok oksigen lima bulan mendatang yaitu Januari 2023 sampai Mei 2023 menggunakan teknik peramalan yang dapat menganalisa perhitungan dengan pendekatan kuantitatif, metode peramalan yang digunakan adalah Double Exponential Smoothing Holt dengan menggunakan perhitungan nilai MAPE  (Mean Percentage Error) untuk menghitung kesalahan peramalan, data yang diteliti merupakan data bulan Januari 2019 hingga Desember 2022 menggunakan alpha = 0,9 dan beta = 0,1 menghasilkan nilai error 2,516% untuk  peramalan penjualan lima bulan mendatang.
Data Warehouse Design For Sales Transactions on CV. Sumber Tirta Anugerah Syaputra, Muhammad Dwiky; Nazir, Alwis; Gusti, Siska Kurnia; Sanjaya, Suwanto; Syafria, Fadhilah
Jurnal CoreIT: Jurnal Hasil Penelitian Ilmu Komputer dan Teknologi Informasi Vol 8, No 2 (2022): December 2022
Publisher : Fakultas Sains dan Teknologi, Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (644.133 KB) | DOI: 10.24014/coreit.v8i2.19800

Abstract

Many data warehouses are implemented in companies engaged in retail, CV. Sumber Tirta Anugerah is one of the paint product retail companies that has not implemented it yet. As time goes by, the sales transaction data is getting more and more difficult to process because it is still stored in Microsoft Excel. This is a serious problem in utilizing historical data to assist in making a decision. It is difficult to store sales data because the data is quite large and a lot. Based on the above problems, a data warehouse design is needed for sales transaction data. This data warehouse design uses Kimball's nine-steps method and star schema. To perform the ETL process (extract, transform, and load) using Pentaho software. In this data warehouse design, Tableau software is used to visualize the processed data into a graph and dashboard report. The result of this research is a data warehouse design using nine steps and a star schema which gets a transformation response time of 4048 MS. 
Penerapan Seleksi Fitur Untuk Klasifikasi Penerima Bantuan Sosial Pangkalan Sesai Menggunakan Metode K-Nearest Neighbor Muhammad Fauzan; Siska Kurnia Gusti; Jasril Jasril; Pizaini Pizaini
Jurnal Sistem Komputer dan Informatika (JSON) Vol 5, No 1 (2023): September 2023
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/json.v5i1.6654

Abstract

The inability to fulfill basic human needs is how poverty is defined. To address this issue, the indonesian goverment implements various social assistance programs, one of which is Kartu Indonesia Pintar (KIP), aimed at providing free education to children aged 7-18 who are economically disadvantaged. However, in the distribution of aid in the Pangkalan sesai sub-district, distributing officers often face challenges due to the high number of eligible recipients applying, complex data requierements, and limited time for the officers. Distributing this social assistance accurately is crusial. Therefore, this research aims to determine the accuracy value for the data of potential recipients of the Kartu Indonesia Pintar (KIP to enhance the data verification process’s outcomes. To tackle this issue, the research employs the K-Nearest Neighbor (K-NN) algoritm and also employs feature selection using Information Gain to reduce less influential attributes. The data used consists of 1998 records of KIP beneficiaries from the 2023 in excel format, with 33 attributes. After performing data cleaning an Information Gain-based feature selection, the dataset is reduced to 1675 records, with 5 selected attributes. The best classification result in this study is achieved with ratios of 7:3 and 8:2, and a value of k = 5, yielding the highest accuracy of 98,21%. The lowest accuracy is obtained using a ratio of 9:1 with the same k value when not using Information Gain, resulting in an accuracy of 89,82%.
Penerapan Algoritma C4.5 Mengklarifikasi Penerimaan Bantuan Sosial Menggunakan Feature Selection M Wandi Dwi Wirawan; Siska Kurnia Gusti; Jasril Jasril; Pizaini Pizaini
Jurnal Sistem Komputer dan Informatika (JSON) Vol 5, No 1 (2023): September 2023
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/json.v5i1.6653

Abstract

The Indonesian government's efforts to overcome poverty in Indonesia are through the Smart Indonesia Card (KIP) program which is carried out by the government in the form of providing assistance to underprivileged families. The main aim of distributing KIP assistance is to help send underprivileged children to continue their education, the difficulties found in receiving KIP are due to the large number of residents registering, as well as the data having several conditions, the limited time available in providing KIP by sub-district parties, the completion base is relatively low, therefore the provision of assistance must be right on target. Therefore, the aim of this research is to look for the most influential attributes in receiving KIP assistance in order to improve the results of the data verification process. After carrying out Feature Selection using Information Gain, the most influential attributes can be obtained. The influences are Number of Art, Number of Rooms, Cooking Room, Refrigerator, Motorbike. Therefore, we need to know some of the attributes that most influence the selection of KIP assistance so that we can get accuracy values from decision tree modeling using the C4.5 algorithm or decision tree. Test This experiment can produce a decision tree in which the Number of Art attribute is the most influential attribute with the success rate of KIP acceptance. This evaluation uses a confusion matrix to obtain an accuracy value of 98.21%, precision of 98.21%, recall of 99.48%.
Klasifikasi Data Penerimaan Zakat dengan Algoritma K-Nearest Neighbor Alfin Hernandes; Siska Kurnia Gusti; Fadhilah Syafria; Lestari Handayani; Siti Ramadhani
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.v4i3.1528

Abstract

National Amil Zakat Agency (BAZNAS) is an institution responsible for managing zakat established by the government. BAZNAS has a presence in every district or city, and one of them is the BAZNAS in the city of Pekanbaru. BAZNAS in Pekanbaru city is responsible for distributing zakat to various empowerment programs, one of which is the Pekanbaru Cares program. Currently, BAZNAS in Pekanbaru city is facing issues related to the method of distributing zakat, where the process of determining the criteria for zakat recipients is still being done manually by the committee of BAZNAS in the city of Pekanbaru. This condition is considered inefficient and poses one of the challenges that need to be addressed. To overcome the mentioned constraints, steps are needed to improve the effectiveness and efficiency of data collection for potential zakat recipients. One of the solutions is to implement a classification system to facilitate the data collection process, using the K-Nearest Neighbor (KNN) method. This approach functions as a tool to classify data for potential beneficiaries. This research aims to classify data and measure the accuracy in assessing the eligibility of zakat recipients based on predetermined criteria, utilizing the K-Nearest Neighbor (K-NN) algorithm. A total of 602 data from BAZNAS in the city of Pekanbaru were used in this study, by dividing the training and test data, namely divided 90:10, 80:20, and 70:30 splits. The evaluation results from the confusion matrix of k=3, k=5, k=7, k=9, and k=11 show that the highest accuracy is achieved at k=5 with an 80:20 split, with an accuracy rate of 89.3%. Furthermore, a precision of 87.3% and a recall of 91.4% can also be attained through this approach.
Perbandingan Triple Exponential Smoothing dan Fuzzy Time Series untuk Memprediksi Netto TBS Kelapa Sawit Raja Indra Ramoza; Siska Kurnia Gusti; Lestari Handayani; Siti Ramadhani
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.v4i3.3433

Abstract

Oil palm plays a crucial role in agriculture and plantations in Indonesia as a commodity with high economic potential. Net Fresh Fruit Bunches (FFB) production is an essential desired outcome in an oil palm plantation. Net FFB is utilized as the primary raw material for the production of Crude Palm Oil (CPO) and Palm Kernel Oil (PKO). The existing challenge is that companies seek to achieve precise quantities and timing for net FFB production in oil palm. One proactive measure to address this is by predicting the net FFB production. Therefore, the objective of this research is to forecast net FFB production by comparing triple exponential smoothing and fuzzy time series methods. Data processing results demonstrate that both forecasting methods yield excellent quality predictions for net FFB production. In the conducted testing, both methods achieved low forecast error values, with MAPE of 11.14670196% and 10.44596891% respectively. However, fuzzy time series exhibited a lower error value compared to the triple exponential smoothing method. Based on these findings, it can be concluded that fuzzy time series is the most reliable model for accurately predicting net FFB production. The advantage of fuzzy time series in forecasting net FFB production can provide significant benefits for companies in determining appropriate strategies for future planning.
Komparasi Metode SAW Dan ANP Dalam Merekomendasikan Penerima Bantuan Covid-19 Muhammad Khairy Dzaky; Eka Pandu Cynthia; Siska Kurnia Gusti
Jurnal Nasional Komputasi dan Teknologi Informasi (JNKTI) Vol 5, No 3 (2022): Juni 2022
Publisher : Program Studi Teknik Komputer, Fakultas Teknik. Universitas Serambi Mekkah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32672/jnkti.v5i3.4415

Abstract

Abstrak - Pada masa sekarang ini, dunia dihebohkan dengan munculnya serangan virus yang disebut dengan Covid-19. Penyebaran virus ini sangatlah cepat sehingga membuat masyarakat menjadi khawatir dan gelisah. Untuk mengurangi penyebaran virus ini, pemerintah pusat menghimbau seluruh masyarakat untuk melaksanakan Pembatasan Sosial Berskala Besar (PSBB), karantina wilayah dan lockdown. Dengan diterapkannya himbauan ini mengakibatkan perekonomian masyarakat menjadi tidak stabil bahkan mengalami penurunan. Hal ini berdampak ke seluruh daerah di Indonesia termasuk salah satunya di Desa Pesisir. Untuk mengurangi dampak sosial ekonomi pandemi Covid-19 di desa Pesisir, pemerintah memberikan bantuan kepada masyarakat berupa bantuan langsung tunai desa dan bahan sembako. Meskipun begitu, bantuan yang diberikan masih belum disalurkan secara optimal, karena masih menggunakan distribusi berdasarkan subjektifitas pegawai kelurahan. Sehingga, konflik antar warga tidak dapat dihindari. Penelitian ini dilakukan untuk mengetahui siapa saja yang berhak untuk menerima bantuan Covid-19 di desa Pesisir yang ditentukan berdasarkan kategori dan kriteria yang telah ditetapkan. Penelitian ini menerapkan metode Simple Additive Weighting (SAW) dan metode Analytical Network Process (ANP) untuk menghasilkan rekomendasi mengenai siapa saja yang berhak menerima bantuan Covid-19 di desa Pesisir.Kata Kunci: Analytical Network Process (ANP), Covid-19, Desa Pesisir, Simple Additive Weighting (SAW),  Abstract - At this time, the world was shocked by the emergence of a virus attack called Covid-19. The spread of this virus is so fast that it makes people become worried and anxious. To reduce the spread of this virus, the central government urges the entire community to implement Large-Scale Social Restrictions (PSBB), regional quarantine and lockdown. With the implementation of this appeal, the community's economy became unstable and even experienced a decline. This has an impact on all regions in Indonesia, including one in the Coastal Village. To reduce the socio-economic impact of the Covid- 19 pandemic in Pesisir villages, the government provided assistance to the community in the form of direct village cash assistance in the form of basic necessities. Even so, the assistance provided is still not optimally distributed, because it is still using a distribution based on the subjectivity of kelurahan employees. Thus, conflicts between citizens cannot be avoided. This research was conducted to find out who is entitled to receive Covid-19 assistance in the Pesisir village which is determined based on the categories and criteria that have been set. This study applies the Simple Additive Weighting (SAW) method and the Analytical Network Process (ANP) method to produce recommendations regarding who is entitled to receive Covid-19 assistance in the Pesisir village.Keywords: Analytical Network Process (ANP), Covid-19, Pesisir village, Simple Additive Weighting (SAW),
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