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All Journal International Journal of Electrical and Computer Engineering IAES International Journal of Robotics and Automation (IJRA) IAES International Journal of Artificial Intelligence (IJ-AI) Bulletin of Electrical Engineering and Informatics Scientific Journal of Informatics Proceeding of the Electrical Engineering Computer Science and Informatics Sistemasi: Jurnal Sistem Informasi Jurnal Teknologi dan Sistem Komputer Informatika Mulawarman: Jurnal Ilmiah Ilmu Komputer SISFOTENIKA IJCIT (Indonesian Journal on Computer and Information Technology) Knowledge Engineering and Data Science IT JOURNAL RESEARCH AND DEVELOPMENT JOURNAL OF APPLIED INFORMATICS AND COMPUTING JURNAL REKAYASA TEKNOLOGI INFORMASI SINTECH (Science and Information Technology) Journal JURNAL TEKNIK INFORMATIKA DAN SISTEM INFORMASI Sebatik ILKOM Jurnal Ilmiah Digital Zone: Jurnal Teknologi Informasi dan Komunikasi MIND (Multimedia Artificial Intelligent Networking Database) Journal Prosiding SAKTI (Seminar Ilmu Komputer dan Teknologi Informasi) EXPLORE TELKA - Telekomunikasi, Elektronika, Komputasi dan Kontrol Jurnal SITECH : Sistem Informasi dan Teknologi Building of Informatics, Technology and Science JISKa (Jurnal Informatika Sunan Kalijaga) Jurnal Teknika Sains, Aplikasi, Komputasi dan Teknologi Informasi Journal of Innovation Information Technology and Application (JINITA) Innovation in Research of Informatics (INNOVATICS) Jurnal Pengabdian UNDIKMA Jurnal Teknik Informatika (JUTIF) Vivabio : Jurnal Pengabdian Multidisiplin Jurnal PTI (Jurnal Pendidikan Teknologi Informasi) JP2KG AUD (Jurnal Pendidikan, Pengasuhan, Kesehatan dan Gizi Anak Usia Dini) JUSTIN (Jurnal Sistem dan Teknologi Informasi) BIOS : Jurnal Teknologi Informasi dan Rekayasa Komputer JAIA - Journal of Artificial Intelligence and Applications EXPLORE Jurnal Bina Komputer JAPI: Jurnal Akses Pengabdian Indonesia Data Sciences Indonesia (DSI) Adopsi Teknologi dan Sistem Informasi Jurnal Masyarakat Madani Indonesia Information Technology Education Journal Journal Of Artificial Intelligence And Software Engineering The Indonesian Journal of Computer Science Inovasi Teknologi Masyarakat Jurnal Pengabdian Siliwangi Jurnal Komputasi
Puspitasari, Novianti
Program Studi Informatika, Fakultas Teknik, Universitas Mulawarman
Religion Aerospace Engineering Agriculture, Biological Sciences & Forestry Humanities Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Health Professions Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Nursing Physics Public Health Social Sciences Other

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Teknologi AI Untuk Meningkatkan Proses Belajar Mengajar Di SMP Patra Dharma 1 Balikpapan Masna Wati; Anindita Septiarini; Novianti Puspitasari; Ummul Hairah; Raudhya Azzahra; Maya Agustina
Jurnal Masyarakat Madani Indonesia Vol. 5 No. 1 (2026): Februari
Publisher : Alesha Media Digital

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59025/4k6zav55

Abstract

Penerapan Artificial Intelligence (AI) meningkat pesat dalam beberapa tahun terakhir. Namun, kurangnya pemahaman terhadap teknologi AI bagi siswa menyebabkan minimnya pemanfaatan AI dalam menunjang proses pembelajaran secara optimal. Edukasi pemanfaatan AI untuk siswa penting dilakukan untuk membantu meningkatkan keterampilan belajar siswa. Metode yang digunakan pada kegiatan ini yaitu pelatihan dan simulasi IPTEKS dimana peserta diperkenalkan tools ChatGPT, QuillBot, Gamma AI dan Runway ML. Kegiatan dilaksanakan di Laboratorium Komputer SMP Patra Dharma 1 Balikpapan selama dua hari dengan peserta sebanyak 46 siswa SMP Patra Dharma 1 Balikpapan kelas VII. Hasil kegiatan menunjukkan bahwa kegiatan ini tidak hanya meningkatkan pengetahuan dan keterampilan siswa dalam penggunaan teknologi AI sebesar 40,61% meskipun dilaksanakan dalam durasi yang relatif singkat, tetapi juga memberikan dampak kualitatif berupa meningkatnya kepercayaan diri dalam menggunakan teknologi, peningkatan kreativitas digital, berkembangnya kemampuan berpikir terstruktur dan kritis. AI secara spesifik berperan sebagai alat bantu yang mempermudah siswa dalam mengembangkan ide, mengorganisasi informasi, serta menyajikan materi dan informasi dalam bentuk digital yang lebih menarik dan interaktif. Peningkatan peserta pada aspek pengetahuan teknologi AI sebesar 41,85%, sedangkan pada aspek keterampilan penggunaan teknologi AI sebesar 39,33%. Dengan pemahaman teknologi AI, siswa mampu mengenali manfaat AI sebagai pendukung proses belajar yang lebih efektif dan menarik
Adaptive Neuro-Fuzzy Inference System for Waste Prediction Haviluddin, Haviluddin; Pakpahan, Herman Santoso; Puspitasari, Novianti; Putra, Gubtha Mahendra; Hasnida, Rima Yustika; Alfred, Rayner
Knowledge Engineering and Data Science
Publisher : citeus

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

Abstract

The volume of landfills that are increasingly piled up and not handled properly will have a negative impact, such as a decrease in public health. Therefore, predicting the volume of landfills with a high degree of accuracy is needed as a reference for government agencies and the community in making future policies. This study aims to analyze the accuracy of the Adaptive Neuro-Fuzzy Inference System (ANFIS) method. The prediction results' accuracy level is measured by the value of the Mean Absolute Percentage Error (MAPE). The final results of this study were obtained from the best MAPE test results. The best predictive results for the ANFIS method were obtained by MAPE of 3.36% with a data ratio of 6:1 in the North Samarinda District. The study results show that the ANFIS algorithm can be used as an alternative forecasting method.
Food Delivery Time Prediction using Tree-Based Ensemble Models: A Comparative Study with Explainable Artificial Intelligence Raihanfitri Adi Kalipaksi; Haviluddin Haviluddin; Anindita Septiarini; Joan Angelina Widians; Novianti Puspitasari
Information Technology Education Journal Vol. 5, No. 3, August (2026)
Publisher : Jurusan Teknik Informatika dan Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59562/intec.v5i3.12157

Abstract

Purpose – Being able to predict delivery time accurately is important for online food delivery services, both for operational efficiency and customer satisfaction. However, this is not an easy task. Delivery time depends on many factors that are related to each other, such as courier characteristics, delivery distance, and order-related information, and these factors interact in complex ways. This study looks at how well tree-based ensemble learning models can predict food delivery time, and also uses explainable artificial intelligence so the models can still be interpreted properly. Design – This study uses 45,593 delivery records taken from Kaggle. In this study, four tree-based ensemble models were developed, namely Random Forest, Gradient Boosting, XGBoost, and LightGBM, with each model optimized through hyperparameter tuning. The models were evaluated using repeated 5-fold cross-validation with three repetitions, and their performance was assessed using Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and R². Findings – LightGBM Tuned came out with the best numerical performance, showing an MAE of 5.678 minutes, RMSE of 7.204 minutes, and R² of 0.411. However, based on ANOVA and Tukey's post-hoc test, the difference in performance between the best boosting-based models was not statistically significant. SHAP analysis also showed that courier rating, delivery distance, courier age, and several interaction features were the factors that had the biggest influence on delivery time prediction. Research implications – The findings suggest that boosting-based ensemble learning models can provide moderate predictive performance while offering interpretable insights into the factors contributing to delivery time predictions. Nevertheless, the moderate R² value indicates that additional operational variables, such as traffic conditions, restaurant preparation time, and courier workload, may be required to improve practical prediction reliability. Originality/value – This study combines ensemble learning, feature engineering, statistical validation, and explainable artificial intelligence together to evaluate both the predictive performance and interpretability of models for food delivery time prediction. 
Penerapan MOORA Pada Sistem Pendukung Keputusan Calon Penerima Bantuan Pangan Non Tunai (BPNT) Sindi Vebi C.G; Novianti Puspitasari; Medi Taruk
Jurnal Rekayasa Teknologi Informasi (JURTI) Vol 10, No 2 (2026): Jurnal Rekayasa Teknologi Informasi (JURTI)
Publisher : Universitas Mulawarman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30872/jurti.v10i2.26979

Abstract

Kemiskinan merupakan salah satu permasalahan sosial yang dihadapi oleh setiap bangsa, termasuk Indonesia. Bantuan sosial merupakan upaya pemerintah dalam menekan angka kemiskinan. Adapun salah satu program yang diusahakan dalam mengentaskan kemiskinan adalah program Bantuan Pangan Non Tunai (BPNT). Namun, pemberian dana BPNT memiliki kendala dalam keterbatasan dana yang akan disalurkan, sehingga proses penentuan penerima BPNT masih belum optimal dan cenderung bersifat subjektif yang menimbulkan permasalahan diantara masyarakat terkait penerima BPNT di Dinas Sosial dan Pemberdayaan Masyarakat yang tidak tepat sasaran. Hal ini menyebabkan warga yang lebih layak menerima BPNT tidak memperoleh haknya dan sebaliknya. Dari permasalahan tersebut penelitian ini membangun sistem pendukung keputusan penerima BPNT untuk membantu pihak Dinas Sosial dan Pemberdayaan Masyarakat dalam menentukan calon penerimah bantuan. Metode yang digunakan dalam penelitian ini adalah metode Multi-Objective Optimization on the basis of Ratio Analysis (MOORA). Metode MOORA digunakan untuk menentukan ranking dari 12 calon penerima BPNT berdasarkan 4 kriteria dari peraturan mentri yang terdiri dari usia, jumlah tanggungan, pekerjaan dan kondisi rumah. Hasil penelitian menunjukkan bahwa alternatif sepuluh (A10) mendapatkan nilai preferensi tertinggi sebesar 30.88 kemudian disusul oleh A5, A7, A1, dan A9 yang direkomendasikan sebagai penerima BPNT.
Detecting Gambling SEO Spam on Academic Domains using Structural and Textual Features: A Domain-Holdout Evaluation Muh Ghazy Daffa Sampe; Muhammad Kevin Adli Pratama; Muhamad Rayhan Akhsani Taqwim; Kalingga Dwindra Putraka; Anindita Septiarini; Novianti Puspitasari
SISTEMASI Vol 15, No 8 (2026): Sistemasi: Jurnal Sistem Informasi
Publisher : Universitas Islam Indragiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v15i8.6457

Abstract

SEO spam practices related to gambling on Indonesian academic domains pose dual risks: they undermine the integrity of institutional websites and impose an operational burden on security teams tasked with distinguishing compromised pages from legitimate content. This study develops a supervised detection approach to identify gambling-related SEO spam pages on .ac.id domains using a rigorously curated reference dataset. The final dataset contains 2,003 manually annotated pages collected from 938 unique domains, comprising 815 malicious pages and 1,188 benign pages. The proposed pipeline combines textual signals from character-level TF-IDF with structural page indicators, including hidden elements, suspicious links, iframes, and external-link patterns. To avoid overly optimistic performance estimates, evaluation was conducted using a domain-holdout protocol, in which 20% of the domains were completely excluded from model training and selection. Five models were compared: a keyword-based baseline, Naïve Bayes with word-level TF-IDF, Random Forest with structural features, and hybrid Logistic Regression and Support Vector Machine (SVM) models. Experimental results on the holdout set show that Random Forest with structural features achieved the highest F1-score of 0.8844, whereas the proposed hybrid SVM achieved the highest precision of 0.9205, with an F1-score of 0.8663. These findings indicate that structural compromise signals are more robust than textual cues in detecting stealthy SEO spam scenarios, while hybrid models remain promising when high precision and interpretability are prioritized.
Perbandingan Holt, Brown DES, dan Theta untuk Peramalan Minyak dan Gas dengan Data Terbatas Amin Padmo Azam Masa; Hamdani Hamdani; Zainal Arifin; Novianti Puspitasari
Building of Informatics, Technology and Science (BITS) Vol 8 No 2 (2026): September 2026
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v8i2.11105

Abstract

The decline and fluctuations in natural resource production in East Kalimantan, particularly oil, natural gas, and coal, create uncertainty regarding future production conditions, necessitating forecasting methods suited to the characteristics of time-series data. However, few studies have specifically compared Holt’s Linear Trend Method, Brown’s Double Exponential Smoothing (Brown’s DES), and the Theta Model using East Kalimantan natural resource data with a relatively limited number of annual observations. This study aims to compare the performance of these three methods in forecasting oil lifting, natural gas lifting, and coal production. The novelty of this study lies in its comparative evaluation of the three methods across three natural resource indicators with different time-series patterns, as well as its assessment of their ability to use limited historical data to generate five-year projections. The Theta Model was included as a comparative method because it can represent time-series patterns through trend decomposition and extrapolation, making it relevant for evaluating data that exhibit long-term changes. The study uses annual data for the 2016–2024 period obtained from the Statistics Indonesia Office of East Kalimantan Province and the East Kalimantan One Data portal. The use of annual data with a relatively limited number of observations was considered when selecting the methods because trend- and smoothing-based approaches such as Holt, Brown’s DES, and Theta can be applied to time series with limited sample sizes without requiring the estimation of a large number of parameters. The research stages included data collection, model development using the three forecasting methods, accuracy evaluation based on Mean Absolute Percentage Error (MAPE), and five-year forecasting. The results show that the Theta Model performed best for oil and natural gas, with MAPE values of 13.04% and 17.42%, respectively. Meanwhile, Brown’s DES produced the lowest MAPE for coal, at 37.35%. The relatively high MAPE values ​​for coal indicate that the three methods have not fully captured the characteristics of the data's fluctuations, and no single forecasting method consistently outperforms the others across all natural resource indicators; therefore, the selection of a method must take into account the pattern characteristics and levels of variation inherent in each time series. The limited accuracy of coal forecasting highlights the need to explore alternative methods capable of capturing fluctuating patterns.
Perbandingan Akurasi LSTM, ARIMA dan Random Forest untuk Prediksi Harga Beras Medium Indonesia Oktaria Indi Cahyani; Tua Delima Sitompul; Faizul Anwar Wandi; Cellia Auzia Nugraha; Nur Fadhilah; Haviluddin; Novianti Puspitasari
Adopsi Teknologi dan Sistem Informasi (ATASI) Vol. 5 No. 1 (2026): Adopsi Teknologi dan Sistem Informasi (ATASI)
Publisher : Mulawarman University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30872/atasi.v5i1.4300

Abstract

Penelitian ini menyelidiki efektivitas peramalan harga rata-rata nasional beras medium II menggunakan data mingguan (Januari 2022–Desember 2024) dengan membandingkan tiga metode: ARIMA, Random Forest (RF), dan Long Short-Term Memory (LSTM). Proses studi meliputi prapemrosesan data, pengembangan model, dan evaluasi menggunakan metrik RMSE, MAE, dan MAPE. Meskipun ARIMA cocok untuk pola linear dan LSTM efektif untuk ketergantungan jangka panjang, Random Forest menunjukkan kinerja superior dalam menangani pola nonlinier dan fluktuasi harga. Hasil evaluasi menunjukkan RF mencapai akurasi tertinggi dengan RMSE 0,08, MAE 0,06, dan MAPE 0,38%, mengungguli LSTM (MAPE 0,51%) dan ARIMA (MAPE 0,73%). Berdasarkan keunggulan ini, RF digunakan untuk memprediksi harga dalam dua belas minggu mendatang, menghasilkan estimasi yang stabil antara Rp15.322 hingga Rp15.326. Penemuan ini menyoroti efektivitas metode ensemble learning (Random Forest) dalam peramalan harga pangan dan ditujukan sebagai dasar rekomendasi kebijakan stabilisasi harga beras nasional.
Klasifikasi Tingkat Penyakit Hipertensi Menggunakan Metode Random Forest Sophy Awaliah; Anindita Septiarini; Novianti Puspitasari
Jurnal Teknika Vol 18 No 2 (2026): SEPTEMBER
Publisher : Universitas Islam Lamongan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30736/jt.v18i2.1660

Abstract

The rising prevalence of hypertension in Indonesia specifically in Samarinda City, which recorded 215,206 cases in 2023 poses challenges for the field of Informatics Engineering regarding the efficiency and consistency of manual disease-level classification in high-patient-volume healthcare facilities. The limitations of conventional approaches create a need for a technology-based system that can classify hypertension severity quickly, accurately, and objectively. This study aims to develop a hypertension severity classification model using the Random Forest algorithm optimized via GridSearchCV and to identify dominant risk factors through Feature Importance analysis. The dataset comprises 5,373 adult patient medical records from the Remaja Community Health Center (Puskesmas) in Samarinda, covering January to December 2024; attributes include gender, age, smoking status, height, weight, systolic and diastolic blood pressure, and a BMI feature derived through feature engineering. Data imbalance was addressed using the SMOTE technique on the training data. Results indicate that the model achieved the highest accuracy of 99.75% with a 70:30 data split, while an 80:20 ratio yielded 99.62% accuracy; both figures exceed the minimum target of 90%. Evaluation using accuracy, precision, recall, and F1-score metrics demonstrates the model's superiority over manual classification. Feature Importance analysis identifies diastolic blood pressure (46.31%) and systolic blood pressure (45.04%) as the most dominant factors, followed by age, BMI, and other attributes. The resulting model can be implemented as a clinical decision support system to assist medical personnel in primary healthcare settings.
Evaluasi Kinerja Fuzzy C-Means dan K-Means dalam Menganalisis Faktor Depresi Mahasiswa Ahmadyani; Muhammad Ariando Ferdian; Christian Mahatma Bimo Alpindo; Muhammad Asadin Nur; Christian Farrel Argya Putra; Anindita Septiarini; Novianti Puspitasari
Jurnal Teknika Vol 18 No 2 (2026): SEPTEMBER
Publisher : Universitas Islam Lamongan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30736/jt.v18i2.1689

Abstract

Depresi di kalangan mahasiswa telah menjadi permasalahan kesehatan mental global yang serius dan berdampak signifikan terhadap kinerja akademik serta kesejahteraan secara keseluruhan. Berbagai faktor seperti tekanan akademik, tekanan finansial, pola tidur, kebiasaan makan, dan hubungan sosial berkontribusi terhadap meningkatnya prevalensi gangguan kesehatan mental di lingkungan kampus. Meskipun perhatian terhadap isu ini semakin berkembang, penelitian terdahulu jarang membandingkan pendekatan data mining berbasis klasterisasi secara spesifik dalam konteks depresi mahasiswa di perguruan tinggi Indonesia. Penelitian ini mengevaluasi kinerja algoritma Fuzzy C-Means (FCM) dan K-Means dalam mengelompokkan mahasiswa berdasarkan faktor-faktor yang berkaitan dengan depresi, menggunakan dataset yang mencakup fitur usia, IPK, kepuasan belajar, durasi tidur, pola makan, riwayat keinginan bunuh diri, tekanan keuangan, riwayat penyakit mental keluarga, dan tingkat depresi. Evaluasi kinerja dilakukan menggunakan Silhouette Score, Davies-Bouldin Index (DBI), dan Sum of Squared Errors (SSE). Hasil menunjukkan bahwa K-Means menghasilkan klaster lebih tegas dengan Silhouette Score 0,18 dan DBI 1,89, serta perbedaan skor depresi signifikan antara Cluster 0 (0,79) dan Cluster 1 (0,23). FCM menghasilkan klaster yang lebih tumpang tindih dengan FPC 0,50 dan Silhouette Score 0,15, yang lebih mencerminkan sifat ambigu dari gejala psikologis. Penelitian ini berkontribusi pada pengembangan kerangka deteksi dini berbasis data untuk depresi mahasiswa.
Penerapan Metode Quick Sort dan Binary Search Pada Sistem Pengelolaan Data Praktikan Dinda Ayu Aprilia; Anton Prafanto; Novianti Puspitasari
Jurnal Komputasi Vol. 14 No. 2 (2026): Jurnal Komputasi
Publisher : Jurusan Ilmu Komputer Fakultas MIPA Universitas Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/komputasi.v14i2.392

Abstract

Pengelolaan data praktikan Pendidikan Agama Islam Fakultas Teknik Universitas Mulawarman sebelumnya masih dilakukan menggunakan beberapa berkas spreadsheet terpisah. Kondisi tersebut menyebabkan proses pencarian, pengurutan, presensi, penilaian, dan rekapitulasi nilai menjadi kurang efisien serta berisiko menimbulkan duplikasi dan kesalahan data. Penelitian ini bertujuan mengembangkan sistem pengelolaan data praktikan berbasis web dengan menerapkan Quick Sort Median of Seven untuk pengurutan dan Binary Search untuk pencarian data. Metode pengembangan sistem menggunakan Waterfall dengan tahapan analisis kebutuhan, perancangan, implementasi, pengujian, dan evaluasi. Sistem dibangun menggunakan Laravel dan MySQL. Data penelitian terdiri atas 879 data praktikan tahun akademik 2024–2025 serta data sintetis berukuran 100, 500, 1.000, dan 5.000 entri untuk pengujian algoritma. Pengujian fungsional dilakukan menggunakan Black Box Testing, sedangkan pengujian kinerja mengukur waktu eksekusi, penggunaan memori, jumlah perbandingan, jumlah pertukaran, jumlah iterasi, serta perbandingan dengan ORDER BY dan WHERE pada MySQL. Hasil pengujian menunjukkan bahwa seluruh fitur utama berjalan sesuai rancangan. Quick Sort Median of Seven menghasilkan waktu eksekusi dan penggunaan memori yang lebih rendah dibandingkan ORDER BY MySQL pada sebagian besar skenario 100 sampai 1.000 data, sedangkan MySQL mulai lebih kompetitif pada beberapa skenario 5.000 data. Binary Search menghasilkan waktu pencarian di bawah 0,03 ms pada seluruh skenario 5.000 data. WHERE Exact dan WHERE LIKE Awalan menunjukkan waktu yang stabil karena dapat memanfaatkan indeks, sedangkan WHERE LIKE Bebas membutuhkan waktu lebih besar pada dataset berukuran besar. Sistem ini membantu pengelolaan data praktikum menjadi lebih terpusat, terstruktur, dan efisien.
Co-Authors Abu Bakar Adelowys Sinaga Adhistya Erna Permanasari Adnan, Fahrizal Afifah, Dinda Nur AHMAD ANSYORI Ahmad Suryadi, Ahmad Ahmad Wahbi Fadillah Ahmadyani Ajay, Muhammad Aji Ayu Muvita Putri Alameka, Faza Alameka, Faza Alfajriani Alfajriani Alfredo Sinaga Alfrina Mewengkang Ali Sholihin Ali, Vigo Santri Alif Rifa’i Almasari Aksenta Alvito Gabbriel Saputra Ambari, Nasser Amin Padmo Azam Masa Andre Ardin Maulana Anindita Septiarini, Anindita Anton Prafanto Arinda Mulawardani Kustiawan Asdar Zulkiawan Awang Harsa Kridalaksana Brins Leonard Pailan Budiman, Edy Cellia Auzia Nugraha Christian Farrel Argya Putra Christian Mahatma Bimo Alpindo Didit Suprihanto, Didit Dinda Ayu Aprilia Ery Burhandenny, Aji Fahrul Agus Fairil Anwar Faizul Anwar Wandi Fajar Fatimah Farisha Rizky Amalia Fathia Nuq Qamarina Fauzan, Ahmad Nur Fayza Virdana Addiza Faza Alameka Faza Alameka Fazma Urmila Jannah Helmi Puadi Firdaus, Muhammad Firdaus, Muhammad Bambang Fornia, Daviana Dwitasari Enka Frans Karta Sayoga Sitohang Fuad, Natalie Gerda, Misselina Madya Gubtha Mahendra Putra Gunawan, Ayu Lestari Gunawan, Santika Haeruddin, Haeruddin Hairah, Ummul Hairah, Ummul Hakim, Muhammad Irvan Hamdani Hamdani Hamdani Hamdani Hamdani Hamdani Hanif, Ahmad Luthfi Hanung Adi Nugroho Hasnida, Rima Yustika Hatta, Heliza Rahmania Haviluddin Haviluddin Haviluddin Haviuddin, Haviluddin Heliza Hatta Heliza Rahmania Hatta, Heliza Rahmania Helmi Puadi, Fazma Urmila Jannah Hemelia, Junita Henderi . Heni Sulastri Herman Santoso Pakpahan Hidayat, Ahmad Nur Hijratul Aini Iin Nurkarima Indah Wulan Lestari Irfan, Aliya Islamiyah Islamiyah Joan Angelina Widians, Joan Angelina Kalingga Dwindra Putraka Kalista, Nazwa Nur Maulida Qintani Kamila, Vina Zahrotun Kurniati, Wendy Kurniawan, Tri Basuki Lalu Delsi Samsumar, M.Eng. Laraswati, Sherina Latifa Gorriana Gusmaningrum Lempas, Gidion M. Rizky Nilzamyahya Maharani, Agustina Dwi Mahendra, Dicky Alvian Masa, Amin Padmo Azam Masna Wati Maya Agustina Medi Taruk Mega Yoalifa Muh Ghazy Daffa Sampe Muhamad Rayhan Akhsani Taqwim Muhammad Abdillah Muhammad Ariando Ferdian Muhammad Asadin Nur Muhammad Dzacky Muhammad Firdaus Muhammad Kevin Adli Pratama Mulia, Amalia Budiana Nataniel Dengen Noval Bayu Setiawan Nur Fadhilah Nur Hasanah Nurhidayat, Rifki Nurkarima, Iin Nursari, Ayla Nurul Kusuma Dewi, Nurul Kusuma Oktaria Indi Cahyani Olivia Octavia Paripurna, Rian Bintang Pasorong, Hillary Bella Patricia Chandra Pebianoor, Pebianoor Prafanto, Anton Pramudya, Pranata Eka Pratama, Fhanji Wilis Pusparini, Faradilla Putra, Gubtha Mahendra Rahayu, Ervina Rahayu, Rizqi Widya Rahmat Kamara Raihanfitri Adi Kalipaksi Raihanfitri Adi Kalipaksi Ramadhaniaty, Dinda Rantetana, Stevie Falentino Raudhya Azzahra Rayner Alfred Rayner Alfred Reza Nur Muhammad Rezky, Muhammad Rima Yustika Hasnida Rita Diana Rizky Pratama Putra Rondongalo Rismawati Rosita, Aliffia Rosmasari Rosmasari Rosmasari Rosmasari Rosmasari Rosmasari, Rosmasari Rosmasari, Rosmasari Saipul, Saipul Sandyanegara, Aryasena Bela Sarira, Brayen Tisra Septiani, Reni D. Septiariani, Anindita Septirini, Anindita Setyadi, Hario Jati Sihombing, Yobel Fernanda Simanungkalit, Julius Rinaldi Sindi Vebi C.G Sophy Awaliah Stefanie Stefanie Sugandi Sugandi Sulastri, Heni Sumaini Sumaini Surya Eka Priyatna Suryani Junita Patandianan Syachmiral, Zidane Althaariq Syarah, May Siti Tejawati, Andi Tjikoa, Ade Fiqri Tua Delima Sitompul Ummul Hairah Vicky Pranandika Wijaksana Wahyudi, Moh Ikhwan Waksito, Alan Zulfikar Wati, Masna Wibisono, Bramantyo Ardi Harimurti Widians, Joan Angelina Wijaya, Zhienka Putri Willyardo Tampubolon Wintin, Chintia Liu Yasmin, Annisa Yuyun Nabilawati Rumbia zahra salsabila Zainal Arifin Zainal Arifin Zali, Wahyu Noor