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All Journal AGRIVITA, Journal of Agricultural Science Jurnal Informatika dan Teknik Elektro Terapan JurTI (JURNAL TEKNOLOGI INFORMASI) Antivirus : Jurnal Ilmiah Teknik Informatika Jurnal ICT : Information Communication & Technology JATI (Jurnal Mahasiswa Teknik Informatika) JISA (Jurnal Informatika dan Sains) JUSTIN (Jurnal Sistem dan Teknologi Informasi) Journal of Renewable Energy, Electrical, and Computer Engineering Journal of Artificial Intelligence and Engineering Applications (JAIEA) PENA ABDIMAS : Jurnal Pengabdian Masyarakat BULLET : Jurnal Multidisiplin Ilmu Jurnal Sistem Informasi dan Manajemen Jurnal Kecerdasan Buatan dan Teknologi Informasi Spektra : Jurnal Ilmu-ilmu Sosial Educommunity Jurnal Pengabdian Masyarakat As-Sulthan Journal of Education Jurnal Komtika (Komputasi dan Informatika)
Irfan Ali, Irfan
Unknown Affiliation
Agriculture, Biological Sciences & Forestry Humanities Automotive Engineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Medicine & Pharmacology Nursing Public Health Social Sciences Other

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PENERAPAN ALGORITMA K-MEANS CLUSTERING UNTUK MENINGKATKAN MODEL PENGELOMPOKAN DAN KINERJA JARINGAN WI-FI SECARA OPTIMAL Fauzan, Akmal; Suarna, Nana; Ali, Irfan; Susana, Heliayanti
Jurnal Informatika dan Teknik Elektro Terapan Vol. 13 No. 2 (2025)
Publisher : Universitas Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/jitet.v13i2.6272

Abstract

Penelitian ini mengimplementasikan algoritma K-Means Clustering untuk menganalisis pola penggunaan jaringan Wi-Fi, guna meningkatkan efisiensi pengelolaan bandwidth dan kualitas layanan. Data berupa kecepatan internet, biaya layanan, dan lokasi pelanggan diolah menggunakan RapidMiner, menghasilkan klaster dengan nilai Davies-Bouldin Index (DBI) sebesar 0.006, menunjukkan kualitas klaster yang sangat baik. Hasilnya memberikan wawasan mendalam tentang segmentasi pelanggan dan pola penggunaan layanan untuk pengambilan keputusan strategis. Algoritma KMeans terbukti efektif dalam optimalisasi sumber daya jaringan, serta menjadi dasar pengembangan sistem monitoring real-time dan teknologi data mining untuk pengelolaan jaringan Wi-Fi skala besar.
Penerapan Convolutional Neural Network (CNN) Untuk Prediksi Penyakit Tanaman Padi Melalui Citra Daun Sariah, Sariah; Suarna, Nana; Ali, Irfan; Solihudin, Dodi
Jurnal Komtika (Komputasi dan Informatika) Vol 9 No 1 (2025)
Publisher : Universitas Muhammadiyah Magelang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31603/komtika.v9i1.12852

Abstract

Penyakit tanaman padi merupakan salah satu faktor utama yang dapat menurunkan hasil produktivitas, terutama di negara agraris seperti Indonesia. Deteksi dini terhadap penyakit ini sangat penting untuk menghentikan pertumbuhan ekonomi lebih lanjut dan mengurangi kemerosotan ekonomi. Masalahnya, identifikasi tanaman padi secara manual membutuhkan banyak waktu dan tenaga, dan seringkali tidak efisien dalam skala besar. Untuk mengatasi masalah ini, tujuan dari penelitian ini adalah mengembangkan model untuk memprediksi penyakit tanaman yang dapat menganalisis gejala penyakit dari citra daun dengan akurasi yang tinggi, sehingga memungkinkan deteksi penyakit dan mitigasi dampak penyakit yang lebih efektif. Metode yang digunakan dalam penelitian ini yaitu algoritma Convolutional Neural Network (CNN) yang memungkinkan pengumpulan data citra daun padi dari berbagai kondisi kesehatan tanaman padi. Dataset yang digunakan dalam penelitian ini berasal dari sumber sekunder dan citra daun padi yang dikumpulkan secara langsung dilapangan. Dataset ini dianalisis menggunakan teknik augmentasi untuk meningkatkan kualitas dan keberagaman data. Berdasarkan hasil penelitian, model CNN terbaik mampu mendeteksi penyakit tanaman padi dengan akurasi hingga 87,43%. Model ini juga menunjukkan tingkat prediksi dan kepercayaan yang tinggi untuk beberapa penyakit kritis, seperti Blast, Blight, Dan Tungro. Hasil penelitian ini menunjukkan potensi CNN dalam membantu petani mendeteksi penyakit tanaman padi, yang pada akhirnya dapat meningkatkan produktivitas dan mengurangi kerugian.
Transformasi Pembelajaran Matematika Melalui Media Pembelajaran Adaptif Berbasis Augmented Reality: Pemberdayaan Guru SMP Di Kota Cirebon Faqih, Ahmad; Ali, Irfan; Kaslani; Adella, Luthfiyyah Iffah; Rayhan, Tubagus Muhammad
PENA ABDIMAS : Jurnal Pengabdian Masyarakat Vol 7 No 1 (2026): Januari 2026
Publisher : LPPM Universitas Pekalongan

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Abstract

Observasi awal di MGMP Matematika SMP Kota Cirebon menunjukkan bahwa pembelajaran matematika masih didominasi metode ceramah (72%) dan pemanfaatan teknologi pembelajaran sangat rendah, hanya 1% guru pernah menggunakan Augmented Reality (AR). Selain itu, guru cenderung menyusun RPP secara seragam tanpa diferensiasi kebutuhan siswa. Program pengabdian ini bertujuan meningkatkan kompetensi guru melalui pelatihan penyusunan RPP adaptif dan pembuatan media pembelajaran berbasis AR. Kegiatan dilakukan dengan pendekatan partisipatif melalui lima tahap: sosialisasi, pelatihan, penerapan teknologi, pendampingan, dan keberlanjutan, melibatkan 20 guru MGMP. Hasil menunjukkan peningkatan signifikan pada dua aspek kompetensi. Sebanyak 90% guru berhasil menyusun RPP adaptif dan mengidentifikasi kebutuhan belajar siswa, dengan peningkatan nilai dari 52,5 menjadi 82,0 (t = 12,87; p = 0,000). Sementara 80% guru berhasil menghasilkan media AR fungsional, dengan peningkatan nilai dari 46,0 menjadi 78,5 (t = 11,23; p = 0,000). Selain itu, terbentuk forum berbagi praktik baik sebagai wujud keberlanjutan program. Program ini efektif meningkatkan kemampuan guru dalam merancang pembelajaran adaptif dan memanfaatkan AR untuk pembelajaran matematika secara inovatif. Kata kunci: matematika, RPP adaptif, augmented reality, MGMP.
Influence of Different Types of Water Absorbent Polymers on Soil Properties, Plant Growth, and Irrigation Interval Dahri, Shahzad Hussain; Mangrio, Munir Ahmed; Shaikh, Irfan Ahmed; Dahri, Zakir Hussain; Ali, Irfan; Mangrio, Abdul Ghafoor; Saleem, Salman; Aqlani, Zaheer Ahmed; Brohi, Sheeraz Aleem; Dahri, Zamin Hussain
AGRIVITA Journal of Agricultural Science Vol 48, No 1 (2026)
Publisher : Faculty of Agriculture University of Brawijaya in collaboration with PERAGI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.17503/agrivita.v48i1.4605

Abstract

Water and minerals are the key resources for citrus production. However, their excessive use can hinder plant growth and lead to economic losses. This study aimed to evaluate the effects of different polymers on soil properties, lemon plant growth, irrigation intervals, and water saving. The treatments were control (T0), water-pad polymer laid at the bottom of the plant pit (T1), polymer in powder form at 3 g/kg of soil (T2), and polymer in crystal form at 3 g/kg of soil (T3). To avoid seepage losses, a plastic polyethylene sheet was placed along each side of the pit, and one-year-old lemon plants were transplanted in each pit. The results show that soil physical and chemical properties significantly improved in all treatments when compared with the control treatment. The improved hydro-physical characteristics increased the irrigation interval and reduced the number of irrigations by 50%. The accumulation of soil minerals (Ca and Mg) and soil cations (K and Na) was significantly increased than control. Water saving of 39% and substantial improvement in plant growth were observed in all polymer treatments. The water and mineral saving and significant improvement in plant growth show the hydrogel as a resilient soil amendment for plant growth and economic benefits.
KOMPARASI ALGORITMA REGRESI LINEAR DAN BACKPROPAGATION NEURAL NETWORK PADA SISTEM PREDIKSI HARGA SAHAM BERBASIS WEBSITE Setiawan, Riyan; Purnamasari, Ade Irma; Ali, Irfan; Rohmat, Cep Lukman; Dwilestari, Gifthera
Jurnal Informatika dan Teknik Elektro Terapan Vol. 14 No. 1 (2026)
Publisher : Universitas Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/jitet.v14i1.8468

Abstract

Penelitian ini bertujuan untuk membandingkan performa algoritma Regresi Linear dan Backpropagation Neural Network dalam memprediksi harga saham PT Astra Agro Lestari serta mengimplementasikannya ke dalam sistem prediksi berbasis web. Data historis saham dari Kaggle digunakan dengan variabel previous, high, low sebagai input dan close sebagai target. Pengembangan sistem menggunakan model Waterfall melalui tahapan analisis kebutuhan, desain, implementasi, pengujian, dan analisis komparatif. Pelatihan model dilakukan menggunakan Scikit-learn untuk Regresi Linear dan TensorFlow/Keras untuk Backpropagation Neural Network, dengan preprocessing MinMaxScaler dan pembagian data latih dan uji sebesar 80:20. Evaluasi model menggunakan Root Mean Squared Error (RMSE) dan Mean Absolute Error (MAE). Hasil pengujian menunjukkan BPNN lebih akurat dengan RMSE 26.81 dan MAE 19.01, dibandingkan Regresi Linear dengan RMSE 45.11 dan MAE 29.56. Sistem web berhasil menampilkan prediksi otomatis, grafik komparatif, dan evaluasi error secara real-time.
ANALISIS SENTIMEN ULASAN APLIKASI BANK JAGO MENGGUNAKAN SUPPORT VECTOR MACHINE DAN NEURAL NETWORK Mariyani, Dinda; Irma Purnamasari, Ade; Ali, Irfan; Nurdiawan, Odi; Nurdiawan, Rudi
Jurnal Informatika dan Teknik Elektro Terapan Vol. 14 No. 1 (2026)
Publisher : Universitas Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/jitet.v14i1.8775

Abstract

Abstrak. Pertumbuhan layanan perbankan digital di Indonesia menjadikan ulasan pengguna pada Google Play Store sebagai sumber penting untuk mengevaluasi kualitas aplikasi, termasuk Bank Jago. Namun, ulasan tersebut bersifat tidak terstruktur, informal, dan mengandung noise sehingga menyulitkan analisis sentimen. Penelitian ini bertujuan memberikan gambaran objektif kecenderungan opini pengguna serta membandingkan kinerja algoritma Support Vector Machine (SVM) dan Neural Network (MLPClassifier). Sebanyak 10.000 ulasan dikumpulkan melalui scraping dan direduksi menjadi 7.946 ulasan setelah penghapusan duplikasi. Data diproses melalui tahapan preprocessing meliputi cleaning, case folding, normalisasi slang, tokenisasi, stopword removal, dan stemming. Pelabelan sentimen dilakukan menggunakan lexicon InSet, sedangkan ekstraksi fitur menggunakan CountVectorizer berbasis Bag-of-Words. Hasil penelitian menunjukkan bahwa SVM memperoleh akurasi tertinggi sebesar 91,2%, lebih unggul dibandingkan Neural Network dengan akurasi 89,8%. Temuan ini menegaskan bahwa pemilihan preprocessing dan representasi fitur yang tepat berperan penting dalam meningkatkan performa analisis sentimen pada ulasan aplikasi perbankan digital. Abstract. The growth of digital banking services in Indonesia has made user reviews on the Google Play Store an important source for evaluating application quality, including Bank Jago. However, these reviews are unstructured, informal, and noisy, creating challenges for sentiment analysis. This study aims to provide an objective overview of user sentiment and to compare the performance of Support Vector Machine (SVM) and Neural Network (MLPClassifier). A total of 10,000 reviews were collected through scraping and reduced to 7,946 reviews after duplicate removal. The data were processed through preprocessing stages including cleaning, case folding, slang normalization, tokenization, stopword removal, and stemming. Sentiment labeling was conducted using the InSet lexicon, while feature extraction employed a Bag-of-Words approach with CountVectorizer. The results show that SVM achieved the highest accuracy of 91.2%, outperforming the Neural Network model with 89.8%. These findings highlight the importance of appropriate preprocessing and feature representation for improving sentiment analysis performance in digital banking application reviews.
ANALISIS SENTIMEN ULASAN PENGGUNA APLIKASI FLO DI GOOGLE PLAY STORE DENGAN MENGGUNAKAN ALGORITMA NAIVE BAYES Kurniawati, Eti; Irma Purnamasari, Ade; Ali, Irfan; Kurniawan, Rudi; Nurdiawan, Odi
Jurnal Informatika dan Teknik Elektro Terapan Vol. 14 No. 1 (2026)
Publisher : Universitas Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/jitet.v14i1.8776

Abstract

Abstrak. Penelitian ini bertujuan untuk menganalisis sentimen ulasan pengguna aplikasi Flo pada Google Play Store menggunakan algoritma Multinomial Naive Bayes. Flo merupakan aplikasi mobile health (mHealth) populer yang digunakan untuk memantau siklus menstruasi dan kesehatan reproduksi. Data dikumpulkan melalui web scraping dan menghasilkan 10.000 ulasan yang setelah pembersihan menjadi 6.908 data valid. Proses pra-pemrosesan meliputi case folding, cleaning, normalisasi, tokenisasi, stopword removal, dan stemming menggunakan Sastrawi. Pelabelan sentimen dilakukan secara semi-otomatis berbasis lexicon InSet dan rating. Ekstraksi fitur menggunakan CountVectorizer menghasilkan representasi Bag-of-Words sebagai input model. Hasil evaluasi menunjukkan bahwa algoritma Naive Bayes mencapai akurasi sebesar 73,6% dengan nilai precision, recall, dan F1-score yang seimbang pada tiga kelas sentimen. Temuan ini menunjukkan bahwa Naive Bayes efektif digunakan dalam mengolah ulasan teks pendek dan informal berbahasa Indonesia. Penelitian ini berkontribusi dalam pemanfaatan machine learning untuk analisis sentimen aplikasi mHealth serta menyediakan wawasan yang dapat digunakan pengembang untuk meningkatkan kualitas layanan aplikasi Flo. Abstract. This study aims to analyze user reviews of the Flo application on Google Play Store using the Multinomial Naive Bayes algorithm. Flo is a popular mobile health (mHealth) application for tracking menstrual cycles and reproductive health. Data were collected using web scraping, obtaining 10,000 initial reviews, with 6,908 valid reviews after cleaning. Preprocessing included case folding, cleaning, normalization, tokenization, stopword removal, and stemming using Sastrawi. Sentiment labeling was performed semi-automatically using the InSet lexicon and rating-based rules. Feature extraction used CountVectorizer with the Bag-of-Words approach. The evaluation shows that Naive Bayes achieved an accuracy of 73.6% with balanced precision, recall, and F1-score across sentiment classes. These results indicate that Naive Bayes is effective for processing short and informal Indonesian text reviews. This research contributes to the application of machine learning in mHealth sentiment analysis and provides insights for developers to improve the quality of the Flo application.
TINJAUAN PUSTAKA: PERAN SEKOLAH ISLAM DALAM MEMBENTUK KESADARAN POLITIK DAN KEWARGANEGARAAN SISWA Fitria, Lailatul; Ali, Irfan; Putriana, Eka; Khairul Anam, Rifqi
As-Sulthan Journal of Education Vol. 3 No. 1 (2026): Januari
Publisher : As-Sulthan Journal of Education

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Abstract

Islamic schools play a strategic role in shaping students’ political and civic awareness amid the increasingly complex socio-political dynamics of Indonesian society. This article aims to systematically examine the role of Islamic schools in cultivating students’ political awareness and civic identity based on findings from previous studies. The method employed is a literature review, analyzing various scholarly sources, including books, journal articles, and policy documents related to Islamic education, civic education, and political education. The findings indicate that Islamic schools contribute significantly through the integration of the national curriculum with Islamic values, particularly within Civic and Pancasila Education, Islamic Religious Education, as well as extracurricular activities and school culture. Islamic values such as justice (al-‘adl), trust (amanah), deliberation (shura), responsibility, tolerance, and social concern serve as ethical foundations in shaping students’ moderate and democratic political attitudes. Moreover, teachers’ roles as role models, participatory school environments, and students’ involvement in school organizations further strengthen the formation of civic awareness. Nevertheless, the review also identifies several challenges, including limited teacher competence, insufficient curriculum integration, and the influence of globalization. Therefore, strengthening political education grounded in Islamic values is essential to developing a generation of Muslims with strong character, political awareness, and responsibility within democratic life.
Deep Learning-Based Consumer Preference Analysis for Batik Packaging Design Using Convolutional Neural Networks Wahyudin, Edi; Bahtiar, Agus; Ali, Irfan; Nurhidayat, Muhammad
JISA(Jurnal Informatika dan Sains) Vol 8, No 2 (2025): JISA(Jurnal Informatika dan Sains)
Publisher : Universitas Trilogi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31326/jisa.v8i2.2494

Abstract

Packaging design plays an essential role in shaping consumers’ first impressions of a product, particularly in the batik industry, where cultural meaning and visual identity are deeply intertwined. This study aims to explore how a Convolutional Neural Network (CNN) can help identify consumer preferences toward various batik packaging designs. The dataset consists of real packaging from local SMEs as well as prototype designs created specifically for this research, incorporating variations in motifs, colors, and structural formats. All images were standardized and normalized to ensure consistency before being processed by the CNN model. The architecture consists of several convolutional layers, pooling layers, and fully connected layers, with dropout applied to reduce overfitting. Model training was conducted using the Adam optimizer and the sparse categorical cross-entropy loss function. The results demonstrate that the model achieved a testing accuracy of 92.51%. Stable performance across precision, recall, and F1-score indicates that the CNN effectively captures visual patterns associated with consumer appeal. These findings highlight the potential for batik SMEs to utilize deep learning as a decision-support tool, enabling them to design packaging that is more appealing, relevant, and aligned with contemporary consumer preferences.
Analysis and Visualization of Sales Transaction Patterns using Decision Tree and Tableau Public Akbar, Miftahul; Rahaningsih, Nining; Ali, Irfan; Dikananda, Fatihanursari; Hayati, Umi
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 5 No. 2 (2026): February 2026
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v5i2.1849

Abstract

This study aims to analyze sales transaction patterns of rubber waste at PT Mandiri Enviro Technosio by integrating the Decision Tree algorithm with interactive visualization using Tableau Public. The dataset consists of 405 sales transactions recorded during the 2024–2025 period, comprising attributes such as transaction date, product type, quantity, unit price, total value, delivery region, and buyer category. The research methodology includes data acquisition, preprocessing to ensure data quality and consistency, construction of a classification model using the CART algorithm, evaluation of model performance through a confusion matrix, and development of interactive dashboards for enhanced interpretability. The Decision Tree model achieved an accuracy of 88.24% in classifying transaction values into low, medium, and high categories. Unit price and transaction period were identified as the most influential attributes in determining transaction value. Visualization using Tableau Public effectively presented the distribution of transaction values, sales trends, and geographical patterns, thereby strengthening analytical insights and supporting data-driven decision making. The integration of classification techniques and interactive visualization contributes to improving business intelligence capabilities and enables the formulation of more adaptive, evidence-based sales strategies.
Co-Authors Abdul Rohim, Adi Nur Abdul Rosid, Rizal Adella, Luthfiyyah Iffah Adi Supriyatna Adinata, Adinata Ahmad Faqih Ahmad Jaelani Akbar, Miftahul Al-Maulid, Hisyam Aldiyansyah, Aldiyansyah Alfin Maulana Alfudola, Mahfudz Alkatiri, Nazwa Alvianatinova, Via Amalia, Rosnita Amer, Abdu Shobarudin Ana Amalia, Ana Andriyanti, Rina Annurfariz, Aditya Apriliana Janatu Marwa Aqlani, Zaheer Ahmed Arofah, Mila Asep Yoyo Wardaya Auliya, Suci Az-Zahra, Asih Azrul, Ahmad Azzam, Ahmad Brohi, Sheeraz Aleem Dahri, Shahzad Hussain Dahri, Zakir Hussain Dahri, Zamin Hussain Dendy Indriya Efendi Destiawati, Deby Dewanty Rafu, Maria Dienwati Nuris, Nisa Dikananda, Arif Rinaldi Dikananda, Fatihanursari Efendi , Dendy Indriya Effendy, Dendy Indria ETI KURNIAWATI Faisal Adam, Faisal Faqih, Habib Fasa, Saefullah Fatmawati, Aisyah FAUZAN, AKMAL Fitria, Lailatul Gifthera Dwilestari Gitacahyani, Adisty Gunia, Euis H Hadiyanto Hayati, Umi Hendiana, Hendiana Hermawan, Ramdan Hidayah, Freni Mega Hidayattullah, Rizky Huda, Irhamul Hurifiani, Alfia Ikbal, Ali Ilham, Mokhamad Indah Indah Indriya Efendi, Dendy Indriyan Dwi Kesuma, Adri Irma Purnama sari, Ade Irma Purnamasari , Ade Irma Purnamasari, Ade Julkarnaen, Agus Juwita, Ita Karbala, Syahid Kaslani Khalda Rifdan, Ghina Lana Sularto Lestari, Gifthera Dwi Listianto, Ahmad Bilal Lisyana, Zita Lukman Rohmat, Cep Mahdalena, Putri Ayu Mangrio, Abdul Ghafoor Mangrio, Munir Ahmed Mariyani, Dinda Martanto . Marthanu, Indra Wiguna Marwah, Sopa Maulana, Ali Mayang Fadilah, Dewi Muharam, Arbi Adi Muharram, Akbar Muhimmatul ulya, Syilwa Nawang Wulan, Hidayah Nining Rahaningsih Nugraha, Rifqi Nugroho, Rizwar Adi Nur Alam, Alfian Nur Aziziah, Aldila Nurdiawan, Rudi Nurhidayat, Muhammad Nursaniah, Rini Nursatika Kusuma, Ines Odi Nurdiawan Pajri, Riki Prahara, Sukma Prihartono, Willy Purnamasari, Ade Irma Putra Pratama, Aeri Putriana, Eka R, Nining Raafi, Muhammad Rahmi Safitri, Rahmi Ramanto, Aditiya Ramdani, Rizki Rayhan, Tubagus Muhammad Rifa'i, Akhmad Rifqi Khairul Anam Rikiyashi, Afkan Rismala, Rismala Rizki Rinaldi, Ade Rizky Wulandhari, Putri Rodhiyana, Mu'allimah Rohman, Dede Rohmat, Cep Lukman Rosyd, Abdul Roziqin, Ahmad Khoirur Rudi Kurniawan Sadiyah, Ainur Rohimatus Salamah, Soviatus Saleem, Salman Saputra, Muhammad Sariah Sariah Setianingsih, Indri Setiawan, Riyan Shaikh, Irfan Ahmed Sholihin Fauzan, Aldi Sofialaela, Annisa Solihudin, Dodi Sri Widyastuti Suarna, Nana Sudrajat, Adi Suryana, Aldi Susana, Heliayanti Susana, Heliyanti Syahrul, Adis Tohidi, Edi Vina, Vina Wahyudin, Edi Windy Mardiyyah, Nita Wirdiyan, Farhan Azfa Wisnu Saputra, Adrian wiwied pratiwi, wiwied Yuslia Devitri Zahrudin Zhahiran Herlambang, Prilanisa