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Ema Utami

Universitas Amikom Yogyakarta

Author-ID : 259410

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Penerapan Simple Additive Weighting pada Pemilihan Canvasser Terbaik PT.Eratel Prima Hardita, Veny Cahya; Utami, Ema; Luthfi, Emha Taufiq
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 6 No 5: Oktober 2019
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Bagi PT. Eratel Prima, canvasser merupakan ujung tombak dalam suatu perusahaan yang bergerak pada bidang pendistribusian produk indosat. Perusahaan ini mengandalkan modal dari Indosat sehingga penilaian Key Performance Indicator sangat penting untuk perusahaan ini. Canvasser sangatlah berpengaruh pada penilaian KPI ini. Sebagai ujung tombak perusahaan, tentu sangat penting untuk memelihara konsistensi dari kinerja canvasser. Untuk memberikan semangat pada canvasser, maka perusahaan memberikan apresiasi berupa bonus setiap bulan bagi canvasser yang memiliki kinerja terbaik. Dalam proses penilaian canvasser terbaik dilakukan oleh Head of Marketing. Namun, proses penilaian masih dilakukan secara penilaian masih subjektif dan tidak relevan dengan situasi aktual. Selain itu, proses penilaian masih manual sehingga membutuhkan waktu lama untuk melakukan pengolahan data. Hal inilah yang menjadi latar belakang untuk membuat sebuah sistem guna memilih canvasser terbaik Metode pengambilan keputusan yang digunakan adalah metode Simple Additive Weighting (SAW). Metode ini digunakan untuk menentukan nilai pembobotan setiap kriteria, yang kemudian dilakukan proses peringkat untuk menentukan alternatif terbaik dari sejumlah alternatif. Hasil perhitungan akhir dari sistem dan hasil perhitungan manual menunjukkan hasil yang sama dengan menggunakan 10 canvasser yang dinilai, dan canvasser mendapat nilai tertinggi yaitu Eko dengan nilai 18.6667 yang terpilih untuk menjadi canvasser terbaik.AbstractFor PT. Eratel Prima, Canvasser is a company’s spearhead for indosat product distribution. Indosat as the investor of this company require a good evaluation of Key Performance Indicator (KPI). Canvasser’s role is very influential for KPI score. As a company’s spearhead, it is very important to maintenance the consistency of canvasser performance. To encourage their spirit, the company give a monthly reward in the form of bonuses for a canvasser with the best performance. The assessment process done by the Head of Marketing. But this assessment process done with manual method and for data process require more time. In addition, the assessment is still subjective and not relevant with actual situation. Based on this problem, it needed a system to select the best canvasser. The used method for decision support system is Simple Additive Weighting (SAW). This method used to determine the weighting value of each criterion, and then carried out a ranking process to determine the best alternative from a number of alternatives. The result of this research are the ranking and further recommendation for the best canvasser.

Modifikasi Fonem Vokal Pada Stemming Kata Tidak Baku Iskandar, Ahmad Fikri; Utami, Ema; Hidayat, Wahyu; Budi, Agung Prasetio; Hartanto, Anggit Dwi
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 10 No 1: Februari 2023
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jtiik.2023105028

Bahasa Indonesia termasuk bahasa yang paling populer digunakan di dunia. Bahasa Indonesia dapat berupa bahasa baku dan tidak baku. Bahasa tidak baku dapat dikarenakan oleh penyerapan dari bahasa asing atau bahasa daerah. Penyerapan ini dapat terjadi perganti huruf vokal. Kontribusi pada penelitian ini adalah melakukan modifikasi fonem pada huruf vokal untuk mengembalikan kata tidak baku ke dalam bentuk kata dasar yang baku disebut sebagai Modified Vocal Phonemes Non Formal. Percobaan dilakukan dengan 60 kata tidak baku yang sudah dilakukan preprocessing pada penelitian sebelumnya terlebih dahulu. Penelitian ini membandingkan hasil algoritma dengan algoritma pada penelitian sebelumnya. Algoritma Modified Vocal Phonemes Non Formal telah berhasil melakukan stemming dengan presisi 90.00% dengan 54 kata tidak baku yang sukses dikonversi ke kata dasar sesuai dengan Kamus Besar Bahasa Indonesia (KBBI) dan 6 kata masih belum berhasil dikonversi. AbstractIndonesian is one of the most popular languages spoken in the world. Indonesian can be standard and non-standard language. Non-standard language can be caused by absorption of foreign languages or village languages. This absorption can occur as a substitute for vowels. The contribution to this research is to modify the phonemes of vowels to return non-formal words into formal root forms known as Modified Vocal Phonemes in Non-Formal. The experiment was carried out with 60 non-formal words that have been preprocessed in the previous research. This research will compare the results of the algorithm with the algorithm in previous research. Algorithm Modified Vocal Phenomes Non-Formal has succeeded in performing stemming with 90.0% precision with 54 words that were successfully converted to base words according to the Big Indonesian Dictionary and 6 words were still not converted.

Pengaruh Komposisi Split Data Terhadap Performa Akurasi Analisis Sentimen Algoritma Naïve Bayes dan SVM Prasetyo, Yoga Adi; Utami, Ema; Yaqin, Ainul
Journal of Electrical Engineering and Computer (JEECOM) Vol 6, No 2 (2024)
Publisher : Universitas Nurul Jadid

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33650/jeecom.v6i2.9188

Analisis sentimen merupakan bidang yang penting dalam pengolahan bahasa alami dan aplikasi sosial media modern. Penelitian ini menginvestigasi pengaruh dari variasi komposisi split data terhadap performa akurasi model analisis sentimen menggunakan SVM dan Naive Bayes. Metode eksperimen menggunakan variasi dari teknik k-fold cross-validation untuk membandingkan hasil dari berbagai proporsi pembagian data latih dan uji. Hasil eksperimen menunjukkan bahwa komposisi split data memiliki dampak signifikan terhadap performa akurasi kedua algoritma, dengan beberapa proporsi split data menghasilkan hasil yang lebih konsisten dan stabil dibandingkan dengan yang lain. Temuan ini memberikan wawasan yang berharga dalam pengaturan praktis untuk pelatihan model analisis sentimen yang lebih efektif dan andal. Teknik ekstraksi fitur yang digunakan Term Frequency-Inverse Document Frequency (TF-IDF), dengan algoritma klasifikasi Naive Bayes dan Support Vector Machine (SVM). Performa model dievaluasi menggunakan metrik seperti akurasi, presisi, recall, dan F1-score. Hasil penelitian menunjukkan bahwa signifikan model SVM dengan rasio 80:20 mencapai akurasi 76,66% dan F1-score 77 %, dibandingkan metode SVM dan Naïve Bayes dengan rasio lainnya.

Riset Jurnal Literatur : Penggunaan Metode Stemming Pada Bahasa Daerah Melayu-Ambon Carolina, Vinnesa Patricia; Utami, Ema; Yaqin, Ainul
Journal of Electrical Engineering and Computer (JEECOM) Vol 6, No 1 (2024)
Publisher : Universitas Nurul Jadid

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33650/jeecom.v6i1.8517

Stemming dalam bahasa Ambon merupakan tantangan yang signifikan karena lexiconnya yang luas, mencakup sekitar 127.000 kata dasar seperti yang tercatat dalam Kamus Besar Bahasa Indonesia. Hal ini disebabkan oleh kompleksitas stemming yang timbul dari tugas untuk mengekstrak kata-kata dasar dari kata-kata yang memiliki imbuhan, yang memerlukan penghapusan berbagai imbuhan seperti awalan, sisipan, akhiran, dan kombinasinya. Proses ini memiliki pentingan yang besar karena sangat memengaruhi kualitas hasil analisis.Untuk mengatasi kompleksitas linguistik ini, beberapa algoritma stemming telah dikembangkan. Algoritma-algoritma ini termasuk Nazief & Adriani, Enhanced Confix Stripping, Sastrawi, dan Tala, masing-masing menawarkan teknik unik untuk menangani kompleksitas stemming dalam bahasa Indonesia. Pemilihan algoritma yang tepat sangat penting untuk memastikan akurasi dan kehandalan proses stemming dalam kerangka analisis.Dalam penelitian stemming yang telah dilakukan, terdapat variasi dalam metode-metode yang digunakan. Algoritma stemming yang paling sering digunakan adalah Nazief & Adriani, dengan 17 kasus tercatat. Kemudian, Enhanced Confix Stripping juga cukup populer dengan 12 kasus. Sastrawi, meskipun dengan frekuensi yang lebih rendah, tetap digunakan dalam 4 kasus. Sedangkan algoritma Tala, meskipun jarang digunakan, tetap muncul dalam 1 kasus. Hal ini mencerminkan diversitas dan pilihan yang tersedia dalam memilih metode stemming yang sesuai dengan kebutuhan penelitian. Meskipun demikian, hal ini mungkin terkait dengan faktor-faktor seperti proyek penelitian yang sedang berlangsung, ketersediaan dana, atau kondisi eksternal lainnya yang memengaruhi produksi penelitian pada periode tersebut. Dengan demikian, penelitian tentang stemming tetap menjadi topik yang menarik dan relevan, dengan potensi untuk terus berkembang dan memberikan kontribusi yang berarti dalam pemrosesan teks dan penelitian linguistik di masa mendatang.

Prediksi Harga Emas Dengan Menambahkan Variabel Covid-19 Menggunakan Metode Naïve Bayes Cahya Pangestu, Galang; Utami, Ema; Dwi Hartanto, Anggit
Journal of Comprehensive Science Vol. 3 No. 1 (2024): Journal of Comprehensive Science (JCS)
Publisher : Green Publisher Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59188/jcs.v3i1.602

COVID-19 atau sebelumnya dikenal sebagai virus corona merupakan sebuah pandemi yang sangat di waspadai di seluruh dunia dan negara Indonesia saat ini. Pandemi COVID-19 dapat mengakibatkan beberapa dampak di berbagai sektor, terutama di sektor ekonomi seperti harga saham dan emas. Emas merupakan salah satu logam mulia yang cukup diminati oleh masyarakat serta dapat berubah sewaktu-waktu sehingga membuat investor perlu untuk melakukan prediksi harga emas. Salah satu metode atau algoritma yang dapat digunakan untuk memprediksi harga adalah Naïve Bayes Classifier (NBC). penelitian ini menggunakan 7 variabel untuk memprediksi harga emas yaitu variabel harga emas masa lalu, kurs dollar, kurs euro, harga minyak mentah dunia, jumlah kasus positif COVID-19, jumlah kasus sembuh dari COVID-19, dan jumlah kematian yang disebabkan COVID-19 di Indonesia. Dataset yang digunakan diperoleh dari berbagai sumber yang terpercaya yang dimulai periode tanggal 2 maret 2020 – 29 oktober 2020, pengujian nantinya akan akan dilakukan dengan berbagai scenario yang berbeda-beda. Hasil pengujian tingkat akurasi tertinggi dalam penelitian ini yaitu sebesar 83% yang terdapat pada 3 scenario pengujian yang berbeda, sedangkan pengaruh variable COVID-19 pada tingkat akurasi menggunakan metode naïve bayes hasilnya tidak terlalu signifikan

Deteksi Tumor Otak Melalui Gambar MRI Berdasarkan Vision Transformers dengan Tensorflow dan Keras Supriadi, Oki Akbar; Utami, Ema; Ariatmanto, Dhani
Jurnal Informatika Universitas Pamulang Vol 8 No 3 (2023): JURNAL INFORMATIKA UNIVERSITAS PAMULANG
Publisher : Teknik Informatika Universitas Pamulang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32493/informatika.v8i3.32707

Brain tumor disease is a serious and complex health problem worldwide. Early and accurate detection of brain tumors has a major impact on patient care and prognosis. Magnetic Resonance Imaging (MRI) has become one of the main diagnostic tools in detecting brain tumors, manual interpretation of MRI images requires high clinical expertise and requires a long time. In recent years, advances in deep learning techniques and image processing have opened up new opportunities in the detection of brain tumors via MRI images. Deep learning techniques, especially the use of Vision Transformers (ViTs) models, have been successful in various complex pattern recognition tasks in images. The Vision Transformers model was chosen due to the performance improvements shown in many image recognition tasks, outperforming convolutional neural networks (CNN) based methods. Tensorflow and Keras are used as frameworks for development and training models, which have been proven effective and efficient in various previous studies. This study focuses on the performance of the Vision Transformer (ViT) in detecting brain tumors through two Magnetic Resonance Imaging (MRI) image datasets, with different numbers of datasets, as well as the maximum accuracy value that can be achieved from the ViT architecture. From several experimental parameters on ViT, the number of datasets and iterations, the results obtained from the first dataset with 253 image data obtained an accuracy value of 88%, and in the second study by combining the two datasets, with 3.123 data images obtained an accuracy of 97.9%.

CATTLE BODY WEIGHT PREDICTION USING REGRESSION MACHINE LEARNING Anjar Setiawan; Utami, Ema; Ariatmanto, Dhani
Jurnal Teknik Informatika (Jutif) Vol. 5 No. 2 (2024): JUTIF Volume 5, Number 2, April 2024
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2024.5.2.1521

Increasing efficiency and productivity in the cattle farming industry can have a significant economic impact. Cow health and productivity problems directly impact the quality of the meat and milk produced. In the cattle farming industry, it can help predict cow weight oriented to beef and milk quality. The importance of predicting cow weight for farmers is to monitor animal development. Meanwhile, for traders, knowing the animal's weight makes it easier to calculate the price of the animal meat they buy. This research aims to predict cow weight by increasing the results of smaller MAE values. The methods used are linear Regressor (LR), Random Forest Regressor (RFR), Support Vector Regressor (SVR), K-Neighbors Regressor (KNR), Multi-layer Perceptron Regressor (MLPR), Gradient Boosting Regressor (GBR), Light Gradient boosting (LGB), and extreme gradient boosting regressor (XGBR). Producing cattle weight predictions using the SVR method produces the best values, namely mean absolute error (MAE) of 0.09 kg, mean absolute perception error (MAPE) of 0.02%, root mean square error (RMSE) of 0.08 kg, and R-square of 0.97 compared to with other algorithm methods and the results of statistical correlation analysis showed several significant relationships between morphometric variables and live weight.

STACKING ENSEMBLE LEARNING AND INSTANCE HARDNESS THRESHOLD FOR BANK TERM DEPOSIT ACCEPTANCE CLASSIFICATION ON IMBALANCED DATASET Bangun Watono; Ema Utami; Dhani Ariatmanto
Jurnal Teknik Informatika (Jutif) Vol. 5 No. 4 (2024): JUTIF Volume 5, Number 4, August 2024 - SENIKO
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2024.5.4.2022

Bank term deposits are a popular banking product with relatively high interest rates. Predicting potential customers is crucial for banks to maximize revenue from this product. Therefore, bank term deposits acceptance classification is an important challenge in the banking industry to optimize marketing strategies. Previous studies have been conducted using machine learning classification techniques with the imbalanced Bank Marketing Dataset from the UCI Repository. However, the accuracy results obtained still need to be improved. Using the same dataset, this study proposes an Instance Hardness Threshold (IHT) undersampling technique to handle imbalanced datasets and Stacking Ensemble Learning (SEL) for classification. In this SEL, Decision Tree, Random Forest, and XGBoost are selected as base classifiers and Logistic Regression as meta classifier. The model trained on SEL with the dataset undersampled using IHT shows a high accuracy rate of 98.80% and an AUC-ROC of 0.9821. This performance is significantly better than the model trained with the dataset without undersampling, which achieved an accuracy of 90.30% and an AUC-ROC of 0.6898. The findings of this research demonstrate that implementing of the suggested IHT undersampling technique combined with SEL has been evaluated to effectively enhance the performance of term deposit classification on the dataset.

Tuberculosis Diagnosis From X-Ray Images Using Deep Learning And Contrast Enhancement Techniques Risma, Vita Melati; Utami, Ema
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 2 (2025): JUTIF Volume 6, Number 2, April 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.2.4315

Tuberculosis (TB) is an infectious disease that poses a global health threat. Early diagnosis through chest X-ray (CXR) imaging is effective in reducing transmission and improving patient recovery rates. However, the limited number of radiologists in high TB burden areas hampers rapid and accurate detection. This study aims to improve TB diagnosis accuracy using deep learning models. Convolutional Neural Networks (CNN) are applied to analyze CXR images to support automated detection in regions with limited radiology personnel. The method involves image processing using Contrast Limited Adaptive Histogram Equalization (CLAHE) to enhance image quality. A public dataset consisting of 2,188 images was used, with preprocessing steps including resizing, normalization, and augmentation. The DenseNet201 model was employed as the main architecture, trained for 10 epochs with various batch sizes to evaluate its performance. Results show that the combination of CLAHE and DenseNet201 achieved the highest accuracy of 94.84%. Image quality enhancement with CLAHE proved to improve accuracy compared to models without preprocessing. This research contributes to enhancing the efficiency of automated early TB detection, reducing reliance on radiologists, and accelerating clinical decision-making.

Improving Infant Cry Recognition Using MFCC And CNN-Based Audio Augmentation Setyoningrum, Nuk Ghurroh; Utami, Ema; Kusrini, Kusrini; Wibowo, Ferry Wahyu
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 2 (2025): JUTIF Volume 6, Number 2, April 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.2.4373

Recognizing infant cries is essential for understanding a baby's needs; however, previous research has struggled with imbalanced datasets and limited feature extraction techniques. Conventional methods utilizing CNN without data augmentation often failed to accurately classify minority classes such as belly pain, burping, and discomfort, resulting in biased models that predominantly recognized majority classes. This study proposes an MFCC-based data augmentation pipeline, incorporating time stretching, pitch scaling, noise addition, polarity inversion, and random gain adjustments to increase dataset diversity and enhance model generalization. By applying this approach, the dataset size was expanded from 457 to 8,683 samples, and a CNN model with three convolutional layers, ReLU activation, and max pooling was trained for cry pattern classification. The results indicate a substantial accuracy improvement from 78% to 98%, with F1-scores for minority classes rising from 0.00 to above 0.90, confirming that augmentation effectively addresses dataset imbalance. This research advances computer science and artificial intelligence, particularly in audio signal processing and deep learning for healthcare applications, by demonstrating the role of data augmentation in improving cry classification performance. Future directions include integrating multimodal data (visual and physiological signals), exploring advanced deep learning architectures, and developing real-time applications for smart baby monitoring systems to further enhance infant cry recognition technology.

Co-Authors , Anggit Dwi Hartanto A.A. Ketut Agung Cahyawan W AA Sudharmawan, AA Abdul Malik Zuhdi Abdullah Ardi Abdulrahmat E Ahmad Abyan Fauzi Widihasani Achmad Yusron Arif Ade Pujianto Adi Surya Adiatma, Biva Candra Lutfi Afif, Muhammad Sholih Afifah Nur Aini Afis Julianto Aflahah Apriliyani Afu Ichsan Pradana Agun Nurul Widiyanto Agung Budi Prasetio Agung Budi Prasetio Agung Budi Prasetio Agung Budi Prasetyo Agung Dwi Cahyanto Agung Nurhidayatullah, Rizqy Agung Susanto Agus Fathurahman Agus Fatkhurohman AGUS PURWANTO Agustin, Tinuk Agustina Srirahayu Ahmad Fauzi Ahmad Febri Diansyah Ahmad Fikri Iskandar Ahmad Fikri Iskandar Ahmad Fikri Iskandar Ahmad Hajar Ahsan, Muhammad Rafiqudin Ahsan, Muhammad Rafiqudin Ainul Yaqin Ainul Yaqin Ainul Yaqin Aji Said Wahyudi Hidayat Aji Triwerdaya Akbar Akbar Akhmad Arif Faisal Siregar Akhmad Dahlan Al Fathir As, Rahmat Saudi Aldy A Kulakat Alfansani, Abdul Rauf Alfin Mahadi Alfrida Sabar Alimuddin Yasin Almi Yulistia Alwanda Alqowiy, Mohd Qorib Alsyaibani, Omar Muhammad Altoumi Alva Hendi Muhammad Alva Hendi Muhammad Alva Hendi Muhammad Alvhinia Meinda Amitaba Alvian Trias Kurniawan Alvian Trias Kurniawan Alvina Felicia Watratan Amali Amali Amir Fatah Sofyan Amir, Fail Amrullah, Ahmad Afief Amrullah, Ahmad Afief Amrullah, Yusuf Amri Andang Wijanarko Andhika Wisnu Widyatama Andhika Wisnu Widyatama Andi Sunyoto Andrie Prajanueri Kristianto Anggi Muhammad Rifai Anggit Dwi Hartanto Anggit Dwi Hartanto Anggit Dwi Hartanto Anggit Dwi Hartanto Anggit Dwi Hartanto Anggit Dwi Hartanto Anggit Dwi Hartanto Anggit Dwi Hartanto Anggit Dwi Hartanto, Anggit Dwi Anggit Hartanto Anggit Rianansyah Anggriandi, Dendi Anip Moniva Anisa Rahmanti Anisya Nursyah Gusman Anjar Setiawan Annisa Rahayu P Anwar Sadad Ardi, Abdullah Arfian Hendro Priyono Arham Rahim Ari Rudiyan Arief Setyanto Arief, M.Rudyanto Arif Nur Rohman Arif Rahman Arif Santoso Arif Sutikno Arif, Achmad Yusron Aris Setiyadi aristin chusnul khotimah Arli Aditya Parikesit Armadiyah Amborowaty Armadyah Amborowati Armadyah Amborowati Armadyah Amborowati Armadyah Amborowati Armadyah Amborowati Armadyah Amborowati Arridho, Muhammad Noor Arvi Pramudyantoro Arya Luthfi Mahadika Asrawi, Hannan Asro Nasiri Asro Nasiri Asro Nasiri Asro Nasiri Asro Nasiri Asrul Abdullah Astica, Yustikamasy Atin Hasanah Aziza Devita Indraswari Baiq Sri Mardiani Bangun Watono Banu Dwi Putranto Basri, Nur Faizal Bayu Setiaji Bety Wulan Sari Bhanu Sri Nugraha Bima Widianto Bisono, Hadi Hikmadyo Biva Candra Lutfi Adiatma Bonifacius Vicky Indriyono Bonifacius Vicky Indriyono, Bonifacius Vicky Brahmantha, Gede Putra Aditya Budi, Agung Prasetio Buyut Khoirul Umri Cahya Pangestu, Galang Candra Adipradana Candra Aditya Pinuyut Carolina, Vinnesa Patricia Catur Iswahyudi Catur Iswahyudi Catur Riyono Heri Wibowo Cecep Yedi Permana Chan Uswatun Khasanah Chavid Syukri Fatoni Christina Andriyani Constantin Menteng D. Diffran Nur Cahyo Dalillah Razan S Danar Putra Pamungkas, Danar Putra Dandi Sunardi Dany Fajar Kristanto Saputro Wibowo David Agustriawan Dede Sandi Dedy Ikhsan Dedy Sugiarto Dengen, Angel Fourtuna Deny Nugroho Triwibowo Desy Bawan, Sarah Bunda Deta Oktariani Dewa Saksana, Jidan Dewi Yustika Lakoro Dhana Aulia Ayu Kurniawan Dhanar Intan Surya Saputra DHANI ARIATMANTO Dhani Ariatmanto Dhani Ratna Sari Dhani Ratna Sari, Dhani Ratna Dibyo Sudarsono Dimaz Arno Prasetio Dina Juni Marianti Dloifur Rohman Al Ghifari Donni Prabowo Donny Yulianto Dwi Ahmad Dzulhijjah Dwi Hartanto, Anggit Dwi Hartono, Anggit Dwi Rahayu Dwi Septiyani Arwin Dwi Yuli Prasetyo Dzulhijjah, Dwi Ahmad Eddy Nurraharjo Eddy Nurraharjo Edhy Sutanta (Jurusan Teknik Informatika IST AKPRIND Yogyakarta) Edi, Mohammad Eko Boedijanto, Eko Eko Darmanto Eko Pramono Eko Pramono Eko Pramono Eko Pramono Eko Purwanto Elim, Marthinus Ikun Elvis Pawan Elvis Pawan Emha Taufik Lutfi Emha Taufiq Luthfi Emilya Ully Artha Emilya Ully Artha Enie Yuliani Enni Lindrawati Eric Ariyanto Ermawan, Bagas Restya Esha Alma'arif Fachruddin Edi Nugroho Saputro Fadhillah, Akmal Rafi Fahmi Ilmawan Fahry, Fahry Fail Amir Faisal Fadhila Fajar Ardanu Fajar Rohman Hariri Fajar Surya Putro Farid Fitriadi Fariz Zakaria Fathoni Dwiatmoko Fatoni, Chavid Syukri Felisberto Pereira Fendi Sumanto Ferry Wahyu Wibowo Ferry Wahyu Wibowo Ferry Wahyu Wibowo Ferry Wahyu Wibowo Fersellia, Fersellia Fidya Farasalsabila Firdaus, M. Haikal Firdiyan Syah Firdiyan Syah Firstyani Imannisa Rahma Firstyani Imannisa Rahma Firza Septian Fitrah Eka Susilawati Fitriana, Frizka Fitriani Fitriani Fitrony, Fachri Ayudi Gabriel Bintang Timur Gardyas Bidari Adninda Gardyas Bidari Adninda Gusti F Rahman Gusti Fathur Rakhman Habib, Muhammad Hafidh Rezha Maulana Hafidz Sanjaya Hafidz Sanjaya, Hafidz Hafiz Ridha Pramudita Hafiz Ridha Pramudita, Hafiz Ridha Halim Bayuaji Sumarna Hamdani, Nahrowi Hamdikatama, Bimantyoso Hanafi Hanafi Hanafi Hanafi Hanafi Hani Setiani Hanif Al Fatta Hardita, Veny Cahya Hartanto, Anggit Dwi Hartatik Hary Susanto Hasna Nirfya Rahmandhani Hedy Leoni Henderi . HENDRA SETIAWAN Hendrawan, Ivan Rifky Hendrik Setiawan, Hendrik Herda Dicky Ramandita Herlandro Tribiakto Hidayat, Jati Arif Hikmianto, Riki Hirmayanti Hudha, Yans Safarid I Dewa Bagas Suryajaya, I Dewa Bagas I Made Adi Purwantara I Wayan Rangga Pinastawa Idris Idris Ikrahmi Ikrahmi Imam Ainuddin P Ina Sholihah Widiati, Ina Sholihah Indarto Indarto Indra Listiawan Irawan, Ridwan Dwi Irawan, Rio Irfan Kurniawan Irma Yanti Irsyad Khalid Ilyas Irwan Siswanto Iskandar, Ahmad Fikri Isra Andika Bakhri Ivan Rifky Hendrawan Ivan Rifky Hendrawan Ivan Rifky Hendrawan Jangkung Tri Nugroho Januario Freitas Araujo Bernardo Jihadul Akbar Juni Marianti, Dina Kartikasari Kusuma Agustiningsih Kasim, Rafli Junaidi Khifni Beyk Ahmad Khoirunnita, Aulia Khusnawi Khusnawi Krisnawati Krisnawati Kriswantoro, Andi Kumara Ari Yuana Kumara Ari Yuawan Kuncoro Hadi Kurniawan, Muhammad Bayu Kusnawi Kusnawi Kusrini Kuswantoro, RB. Hendri Langgeng Hadi Prasetijo Lewu, Retzi Lindrawati, Enni Lisa Dinda Yunita M Imam Budi Laksamana M. Imam Budi Laksamana M. Imam Budi Laksamana M. Nuraminudin M. RUDYANTO ARIEF M. Rudyanto Arief M. Rudyanto Arief M. Rudyanto Arief M. Rudyanto Arief M. Suyanto M. Suyanto, M. M. Syafri Lamato M. Ulil Albab M. Zainal Arifin M. Ziaurrahman Ma'ruf Aziz Muzani Mahdi Ridho Mahmud Zunus Amirudin Mardi Utomo Marianti, Dina Juni Maringka, Raissa Marselina Endah Hiswati Martina Endah Pratiwi Maulana Brama Shandy Megantara, Nugraha Asthra MEI PARWANTO KURNIAWAN Miftah Alfian Firdausy Mochammad Yusa Mochammad Yusa Mochammad Yusa Mochammad Yusa, Mochammad Moh Muhtarom Mohammad Edi Monalisa Fatmawati Sarifah Moniva, Anip Mudawil Qulub Muh Adha Muh Adha Muh Wal Ikram Muh Wal Ikram Muhamad Arldi Megantara Muhamad Ekhsan Muhamad Fatahillah Z Muhamad Fatchan Muhamad Paliya Sadana Muhamad Ridwan Muhammad Agung Nugroho Muhammad Akbar Maulana Muhammad Altoumi Alsyaibani Muhammad Anwar Fauzi Muhammad Arfina Afwani Muhammad Bayu Kurniawan Muhammad Fajrian Noor Muhammad Firdaus Abdi Muhammad Ilyas Prakanada Muhammad Iqbal Muhammad Lathifuddin Arif Muhammad Paliya Sadana Muhammad Resa Arif Yudianto Muhammad Ricky Perdana Putra Muhammad Ricky Perdana Putra Muhammad Rizky Hajar Muhammad Rosikhu Muhammad Rusdi Rahman Muhammad Surahmanto Muhammad Suyanto Muhammad Syaiful Anam Muhammad Syukri Mustafa Muhammad Syukri Mustafa, Muhammad Syukri Mukhadimah Mukhlishah, Aiman Mursyid Ardiansyah Mutiara Dwi Anggraini NABILA OPER NAHROWI HAMDANI Nahrun Hartono Nahrun Hartono, Nahrun Nalda Kresimo Negoro Napianto, Riduwan Nasiri, Asro Ngaeni, Nurus Sarifatul Ngajiyanto, Ngajiyanto Ni Nyoman Utami Januhari Nita Helmawati Nova Agustina Nova Noor Kamala Sari Nugroho Setio Wibowo Nugroho, Jangkung Tri Nuk Ghurroh Setyoningrum Nur Hamid Sutanto Nur Hamid Sutanto Nur?aini, Nur?aini Nura Nugraha, Icha Nurcahyo, Azriel Christian Nurfaizah Nurfaizah Nurfajri Asfa Nurhasan Nugroho Nuri Cahyono Nurmasani, Atik Nurul Ilma Hasana Kunio Nurul Muttaqien Nurul Pratiwi, Annisa Octhavia Alin Okfan Rizal Ferdiansyah Oktariani, Deta Olivia Maria Inacio Tavares Omar Muhamammad Altoumi Alsyaibani Omar Muhammad Altoumi Alsyaibani Pangera, Abas Ali Patmawati Hasan Pebri Antara Prabowo Budi Utomo Pranata, Caraka Aji Prasetio, Agung Budi Prasetyo, Ade Prasetyo, Yoga Adi Pratama, Rendy Bagus Pratama, Zudha Prayoga, Dimas Prayoga, Mahendra Bayu pujiharto, eka wahyu Pulungan, Linda Nurul Taqwa Purnawan Purnawan Purnawan Purwidiantoro, Moch. Hari Purwoko, Agus Putu Putrayasa Qolbun Salim As Shidiqi Qolbun Salim As Shidiqi Quratul Ain Raden Bagus Bambang Sumantri Raditya Maulana Anuraga Rahardyan Bisma Setya Putra Rahmad Ardhani Rahmat Rahmat Rahmat Taufik R.L Bau Rahmatullah, Sidik Rakhma Shafrida Kurnia Ramadoni, Ramadoni Rantung, Tessa Vatma Rasyida, Zulfa Raynaldi Fatih Amanullah Resty Wulanningrum Reyhan Dwi Putra Reyhan Dwi Putra Rhomita Sari Ria Andriani Ricki Firmansyah Ricky Ferdiansyah Rifki Fahmi Rifqi Anugrah Rifqi Mizan Aulawi Riska K Abdullah Riska Kurniyanto Abdullah Risma, Vita Melati Rismayani Rismayani Riyanto Riyanto Rizki Firdaus Mulya Rizky Arya Kurniawan Rizky Handayani Rizky Handayani Rizqa Luviana Musyarofah Rodney Maringka Rodrigo Martínez Béjar Ronaldus Morgan James Roshandri, Wien Fitrian Roshandri, Wien Fitrian S, Muhammad Sabri Safor Madianto Saiful Bahri Salibana, Chlyfen Richard Samsul Bahri Samuel Adhi Bagaskoro Santi Santi Sapta Hary Surya Wibowo Saputra, Artha Gilang Saputra, Artha Gilang Sari, Rita Novita Sari, Yunita Sartika Sarkawi - Sartje Mala Rangkoly Satyo Widijanuarto Selamet Riadi Selvi Marcellia Selvy Megira Setiawan Budiman Setiawan, Bambang Abdi Setiawan, Hendi Setya Putra, Rahardyan Bisma Sidiq Wahyu Surya Wijaya Sigit Sugiyanto Sigit Suryono Siswo Utomo, Mardi Slameto, Andika Agus Sofyan Pariyasto Sofyawati, Siti Sri Hartati Sri Hartati Sri Yanto Qodarbaskoro Subastian Wibowo Sudarmawan Sudarmawan Sudarmawan, Sudarmawan Sudirman, San Sukoco Sukoco Sukoco Sukoco Sukrisno Amikom Suliswaningsih Suliswaningsih Suparyati Suparyati Supriadi, Oki Akbar Surya Ade Saputera Surya, Satria Dwi Suryono, Sigit Suryono, Wachid Daga Sutanto, Nur Hamid Sutrisno Sutrisno Suwanto Raharjo Suwanto Suwanto Suwondo, Adi Suyadi - Suyatmi Suyatmi Swastikawati, Claudia Syah, Firdiyan Syah, Firdiyan Syahrudin, Erwin Syarham, Syarham Tamaulina Br Sembiring Tamrizal A. M. Tamsir, Kurniawati Tantoni, Ahmad Tantoni, Ahmad Teguh Ansyor Lorosae Tigus Juni Betri Tika Novita Sari Tikasni, Elisa Tinuk Agustin Tommy Dwi Putra TONNY HIDAYAT Toto Indriyatmoko Toto Rusianto Tri Amri Wijaya Tri Yusnanto Triana Triana Tuhpatussania, Siti Tutut Maitanti Ulinuha, Hinova Rezha Utama, Hastari Veny Cahya Hardita Verra Budhi Lestari Vian Ardiyansyah Saputro Wahyu Ciptaningrum Wahyu Hidayat Wahyu Hidayat Wahyu Hidayat Wahyu Hidayat Wahyu Hidayat Wahyu Hidayat Wahyu Hidayat Wahyu Hidayat Wahyudi Hidayat, Aji Said wahyuni, wenti ayu Wicaksono, Sherif Aji Widjiyati, Nur Wijaksana, Candra Putra Wijaya, Tri Amri Yans Safarid Hudha Yanuargi, Bayu Yaqin, Aiinul Yefta Tolla Yetman Erwadi Yohanes Aryo Bismo Raharjo Yosef Murya Kusuma Ardhana Yulianto Mustaqim Yulita Fatma Andriani Yumarlin MZ Yusa, Mochammad Zitnaa Dhiaaul Kusnaa Washilatul Arba'ah Zitnaa Dhiaaul Kusnaa Washilatul Arba’ah Zitnaa Dhiaaul Kusnaa Washilatul Arba’ah Zulfa Rasyida Zulpan Hadi

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