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All Journal Jurnal Informatika Indonesian Journal of Artificial Intelligence and Data Mining Seminar Nasional Teknologi Informasi Komunikasi dan Industri Jurnal Sains dan Informatika Jurnal Informatika Universitas Pamulang Jurnal Nasional Komputasi dan Teknologi Informasi JURIKOM (Jurnal Riset 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) KLIK: Kajian Ilmiah Informatika dan Komputer
Siska Kurnia Gusti
Universitas Islam Negeri Sultan Syarif Kasim Riau
Aerospace Engineering Automotive Engineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Mathematics Neuroscience Transportation

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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%.
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.
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%.
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),
Klasifikasi Berita Menggunakan Metode Support Vector Machine Robbi Nanda; Elin Haerani; Siska Kurnia Gusti; Siti Ramadhani
Jurnal Nasional Komputasi dan Teknologi Informasi (JNKTI) Vol 5, No 2 (2022): April 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.v5i2.4193

Abstract

Abstrak - Berita adalah sebuah informasi mengenai peristiwa yang terjadi di suatu lokasi yang bisa disajikan dalam bentuk teks maupun visual. Berita bisa ditemukan di berbagai portal berita dan media cetak. Umumnya setiap berita dikelompokan berdasarkan kategori umum seperti ekonomi, politik, olahraga, dll. Permasalahan yang muncul adalah  bagaimana cara untuk melakukan pengelompokan pada data berita yang biasanya berjumlah hingga ribuan karakter kedalam kategori yang lebih spesifik. Permasalah ini dapat diselesaikan dengan cara menerapkan text mining dengan memanfatakan algoritma klasifikasi untuk mendapatkan sebuah model fungsi yang merepresentasikan tiap kategori berita. Salah satu algoritma klasifikasi yang cukup tangguh untuk melakukan proses klasifikasi teks adalah Support Vector Machine. Penelitian ini menggunakan 510 data berita dengan batasan klasifikasi 3 kategori berita. Algoritma SVM mendapatkan hasil akurasi tertinggi di 88% untuk nilai parameter C =1, kernel Linear dengan pembagian data uji dan data latih sebesar 90% dan 10 %.Kata kunci : Berita, Klasifikasi, Support Vector Machine, Text Mining Abstract  - News is information about events that occur in a location that can be presented in text or visual form. News can be found on various news portals and print media.Generally each news is grouped by general categories such as economics, politics, sports, etc. The problem is how to group news data into more specific categories.This problem can be solved by applying text mining using the classification algorithm to obtain a function model that represents each news category. One of the classification algorithms that is strong enough to do the text classification process is the Support Vector Machine. This study uses 510 news sample with a classification limit of 3 news categories. The SVM algorithm gets the highest accuracy at 88% for the parameter value C = 1, and Linear kernel with the distribution of test data and training data is 90% and 10%.Keywords : Classification, News, Support Vector Machine, Text Mining
Klasifikasi Berita Menggunakan Algoritma C4.5 Yayuk Wulandari; Elin Haerani; Siska Kurnia Gusti; Siti Ramadhani
Jurnal Nasional Komputasi dan Teknologi Informasi (JNKTI) Vol 5, No 2 (2022): April 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.v5i2.4194

Abstract

Abstrak - Perkembangan zaman mengalami kemajuan yang sangat pesat, saat ini penyebaran berita yang paling populer adalah melalui internet. Berita yang disajikan di situs berita online biasanya hanya dalam kategori umum, sehingga ketika pembaca ingin mendapatkan kategori berita yang lebih spesifik harus dilakukan secara manual yang tentunya menjadi kegiatan yang cukup merepotkan. Hal ini juga dialami oleh Badan Pusat Statistik Provinsi Riau yang kesulitan dalam mencari dan mengklasifikasikan berita tentang Provinsi Riau. Dalam hal ini penerapan klasifikasi otomatis dirasa sangat diperlukan. Penelitian ini menggunakan metode klasifikasi C4.5 dengan 510 data berita yang akan diklasifikasikan menjadi 3 kategori yaitu demokrasi, kemiskinan dan ketenagakerjaan. Proses klasifikasi berita dalam penelitian ini meliputi: pengumpulan data, pelabelan manual, teks preprocessing, pembobotan kata, dan metode klasifikasi C4.5. Berdasarkan penelitian yang dilakukan, hasil uji akurasi adalah 84% dengan menggunakan pembagian data 90%:10%. Dapat disimpulkan bahwa metode C4.5 cocok digunakan dalam klasifikasi berita.Kata kunci: Badan Pusat Statistik, Berita, C4.5, Klasifikasi. Abstract - The development of the times has progressed very rapidly, currently the most popular spread of news is through the internet. The news presented on online news sites is usually only in general categories, so when readers want to get a more specific category of news, it must be done manually, which of course will be a bit of a hassle. This is also experienced by the social sector of the Badan Pusat Statistik of Riau, which has difficulty finding and classifying news about Riau Province. In this case the application of automatic classification is felt to be very necessary. This study uses the C4.5 classification method with 510 news data which will be classified into 3 categories, namely democracy, poverty and employment. The news classification process in this study includes: data collection, manual labeling, preprocessing text, word weighting, and C4.5 classification method. Based on the research conducted, the results of the accuracy test were 84% using 90%:10% data sharing. It can be concluded that the C4.5 method is suitable for use in news classification.Keywords : Badan Pusat Statistik, C4.5, Classification, News.
Co-Authors Abdullah Abdullah Abdullah, Said Noor Abdussalam Al Masykur Alfin Hernandes Alwis Nazir Alwis Nazir Anggi Vasella Beni Basuki Devi Julisca Sari Dina Septiawati Eka Pandu Cynthia Elin Haerani Elin Haerani Fadhilah Syafria Febi Yanto Fitri Insani Fitri Wulandari Iwan Iskandar Jasril Jasril Lestari Handayani Maemonah, Maemonah Morina Lisa Pura Muhammad Fauzan Muhammad Khairy Dzaky Muhammad Rifaldo Al Magribi Norhiza, Fitra Lestari Nurul Ikhsan Pizaini Pizaini Raja Indra Ramoza Risfi Ayu Sandika Robbi Nanda Satria Bumartaduri Siti Ramadhani Siti Ramadhani Suwanto Sanjaya Umam, Isnaini Hadiyul Wulandari, Fitri Yayuk Wulandari Yola, Melfa Yusra Yusra, Yusra