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All Journal AKADEMIKA Jurnal Nasional Komputasi dan Teknologi Informasi PengabdianMu: Jurnal Ilmiah Pengabdian kepada Masyarakat Specta Journal of Technology KLIK: Kajian Ilmiah Informatika dan Komputer Jurnal Informatika Teknologi dan Sains (Jinteks) Jurnal Teknologi Informasi Cyberku Jurnal Ilmu Komputer Jurnal Sistem Informasi dan Bisnis Cerdas Jurnal Abdimas Terapan Jurnal Teknologi Informasi Indonesia Jurnal Penelitian Pendidikan Indonesia Riau Jurnal Teknik Informatika Jurnal Nasional Komputasi dan Teknologi Informasi Jurnal Repositor

Alter Lasarudin

Universitas Muhammadiyah Gorontalo

Author-ID : 4278771

Aerospace Engineering Agriculture, Biological Sciences & Forestry Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Engineering Environmental Science Immunology & microbiology Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Neuroscience Public Health Social Sciences Transportation Other

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Perbandingan Algoritma FP-Growth dan Apriori untuk Menganalisis Pola Pembelian Konsumen Noviyanti Patak Lembang; Alter Lasarudin; Wahyudin Hasyim
Jurnal Nasional Komputasi dan Teknologi Informasi Vol. 9 No. 2 (2026): April, 2026
Publisher : Program Studi Teknik Komputer, Fakultas Teknik. Universitas Serambi Mekkah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32672/n5gaj075

Abstrak - Pemamfaatan data transaksi penjualan berperan penting dalam mengungkap pola pembelian konsumen yang mendukung pengambilan keputusan usaha, namun pada praktiknya masih belum dimanfaatkan secara optimal oleh Usaha Kecil dan Menengah. Penelitian ini bertujuan untuk menerapkan serta membandingkan algoritma FP-Growth dan Apriori dalam menganalisis pola pembelian konsumen pada UKM Mart Koperasi Wanita Kasih Ibu. Penelitian ini menggunakan pendekatan Kuantitatif dengan teknik Association Rule Mining terhadap data transaksi penjualan periode Januari hingga Desember 2024, dengan jumlah data awal sebanyak 9305 transaksi dan setelah proses pembersihan diperoleh 7972 transaksi yang terdiri dari dua atribut. Analisis data dilakukan menggunakan aplikasi RapidMiner. Hasil penelitian menunjukkan bahwa algoritma FP-Growth menghasilkan 55 aturan asosiasi dengan waktu pemrosesan yang 1 detik, sedangkan algoritma Apriori menghasilkan 57 aturan asosiasi dengan waktu pemrosesan 4 detik. Penelitian ini menunjukkan bahwa algoritma FP-Growth lebih efektif digunakan dalam mengidentifikasi pola pembelian konsumen sebagai dasar penyusunan strategi pemasaran dan pengelolaan persediaan. Kata kunci : Data Mining; Association Rule Mining; FP-Growth; Apriori; Pola Pembelian Konsumen; Abstract - Utilization Sales transaction data plays an important role in revealing consumer purchasing patterns that support business decision-making, but in practice it is still not optimally utilized by Small and Medium Enterprise. This study aims to apply and compare the FP-Growth and Apriori algorithms in analyzing consumer purchasing pattern at UKM Mart Koperasi Wanita Kasih Ibu. This study uses a quantitative approach with the technique of Association Rule Mining the analysis used sales transaction data from January to Desember 2024, with an initial data set of 9,305 transactions and after the cleaning process, 7,972 transactions consisting of two attributes were obtained. Data analysis was performed using the RapidMiner application. The results showed that the FP-Growth algorithm generated 55 association rules with a processing time of 1 second, while the Apriori algorithm generated 57 association rules with a processing time of 4 seconds. This study indicates that the FP-Growth algorithm is more effective in identifying consumer purchasing patterns as a basic for developing marketing strategies and inventory management. Keywords: Data Mining; Association Rule Mining; FP-Growth, Apriori; Consumer Purchasing Patterns;

Klasifikasi Hard Skill dan Soft Skill IT dari Iklan Kerja Menggunakan Metode Berbasis Rule-Based dan Similarity Matching Hapsa Husain; Wahyudin Hasyim; Alter Lasarudin; Tri Pratiwi Handayani
Jurnal Nasional Komputasi dan Teknologi Informasi Vol. 9 No. 3 (2026): Juni, 2026
Publisher : Program Studi Teknik Komputer, Fakultas Teknik. Universitas Serambi Mekkah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32672/pf66v794

Abstrak - Kesenjangan kompetensi antara lulusan perguruan tinggi dengan kebutuhan aktual industri Teknologi Informasi (TI) masih menjadi kendala utama dalam penyerapan tenaga kerja. Penelitian ini bertujuan untuk mengklasifikasikan kompetensi hard skill dan soft skill yang diekstraksi dari data iklan lowongan kerja guna memetakan kebutuhan industri secara presisi. Metodologi yang diterapkan menggunakan pendekatann yang menggabungkan metode rule-based untuk ekstraksi entitas kompetensi serta similarity matching dengan algoritma Cosine Similarity untuk proses pemetaan terhadap Standar Kompetensi Kerja Nasional Indonesia (SKKNI). Penelitian ini mengolah data sebanyak 701 iklan lowongan kerja IT dengan merujuk pada 2.126 unit kompetensi SKKNI yang mencakup 57 okupasi. Hasil penelitian menunjukkan bahwa penggunaan metode Rule-Based dan Similarity Matching mampu mengidentifikasi variasi kompetensi pada teks iklan kerja yang tidak terstruktur dengan performa sangat baik, dibuktikan dengan nilai akurasi sebesar 88,02%, presisi 88,09%, recall 96,92%, dan F1-score 92,29%. Berdasarkan Hasil penelitian mengungkap dominasi kebutuhan hard skill (62.0%) dengan prioritas pada bidang keamanan siber (security) dan basis data, sementara soft skill (38,0%) didominasi oleh kemampuan komunikasi dan pemecahan masalah. Kerangka kerja ini diharapkan dapat membantu institusi pendidikan dalam sinkronisasi kurikulum serta menjadi panduan bagi pencari kerja dalam memetakan kompetensi spesifik yang dibutuhkan industri. Kata Kunci: Hard Skill; Soft Skill; IT; Rule-Based; Cosine Similarity; SKKNI; Iklan Kerja; Abstract - The competency gap between university graduates and the actual needs of the Information Technology (IT) industry remains a major obstacle to workforce absorption. This study aims to classify hard and soft skill competencies extracted from job advertisement data to precisely map industry needs. The applied methodology uses a rule-based approach for extracting competency entities and similarity matching with the Cosine Similarity algorithm for mapping against the Indonesian National Work Competency Standards (SKKNI). This study processed data from 701 IT job advertisements, referring to 2,126 SKKNI competency units covering 57 occupations. The results of the study indicate that the Rule-Based and Similarity Matching method is capable of identifying competency variations in unstructured job advertisement text with excellent performance, as evidenced by an accuracy of 88.02%, a precision of 88.09%, a recall of 96.92%, and an F1-score of 92.29%. The results revealed a dominant need for hard skills (57.8%), with a priority on cybersecurity and databases, while soft skills (42.2%) were dominated by communication and problem-solving abilities. This framework is expected to assist educational institutions in curriculum synchronization and serve as a guide for job seekers in mapping specific competencies needed by the industry. Keywords: Hard Skills; Soft Skills; IT; Rule-Based; Cosine Similarity; SKKNI; Job Advertisements;

Analisis Penggunaan Large Language Model (LLM) dalam Proses Belajar Mahasiswa Miranda A. Hasan; Alter Lasarudin; Frangky Tupamahu
Jurnal Nasional Komputasi dan Teknologi Informasi Vol. 9 No. 3 (2026): Juni, 2026
Publisher : Program Studi Teknik Komputer, Fakultas Teknik. Universitas Serambi Mekkah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32672/bcmrjx78

Abstrak - Penelitian ini bertujuan untuk memetakan pola penggunaan LLM, mengevaluasi tingkat efisiensi proses belajar, serta mengukur dampak kompetensi teknis prompt engineering mahasiswa terhadap validitas informasi di Universitas Muhammadiyah Gorontalo. Jenis penelitian adalah kuantitatif dengan pendekatan deskriptif. Jumlah sampel sebanyak 96 mahasiswa aktif dari berbagai tahun angkatan yang dipilih menggunakan teknik Proportionate Stratified Random Sampling. Analisis data dilakukan secara deskriptif dan perhitungan Tingkat Capaian Responden (TCR) menggunakan bantuan software SPSS. Hasil penelitian menunjukkan bahwa LLM telah menjadi bagian dari aktivitas akademik rutin mahasiswa, di mana platform yang paling dominan digunakan adalah ChatGPT (38,5%) dengan jenis tugas terbanyak adalah mencari referensi (34,5%). Dari aspek efisiensi belajar, indikator efisiensi waktu memperoleh TCR tertinggi sebesar 88% (Kategori Baik/Efisien), sementara indikator beban verifikasi memperoleh skor terendah sebesar 74% (Kategori Cukup). Pada variabel prompt engineering dan risiko halusinasi, aspek validasi output meraih TCR tertinggi sebesar 78,48%, sedangkan kesadaran risiko halusinasi berada pada angka 73,89%. Dapat disimpulkan bahwa penggunaan LLM dinilai efektif dalam mengakselerasi produktivitas belajar mahasiswa, namun penerapannya tetap membutuhkan sikap kritis dan verifikasi mandiri yang ketat agar hasil belajar yang diperoleh tetap valid dan akurat Kata kunci : Large Language Model; Efisiensi Belajar; UTAUT; Prompt Engineering; Hallucination Trap; Abstract – This study aims to map the patterns of LLM usage, evaluate the efficiency level of the learning process, and measure the impact of students' technical competence in prompt engineering on information validity at Universitas Muhammadiyah Gorontalo. This type of research is quantitative with a descriptive approach. The sample consisted of 96 active students from various class years selected using the Proportionate Stratified Random Sampling technique. Data analysis was conducted descriptively along with the calculation of the Respondent Achievement Level (TCR) using SPSS software. The results showed that LLMs have become a part of students' routine academic activities, where the most dominant platform used is ChatGPT (38.5%) and the most frequent type of academic task assisted is searching for references (34.5%). From the aspect of learning efficiency, the time efficiency indicator obtained the highest TCR of 88% (Good/Efficient Category), while the verification burden indicator obtained the lowest score of 74% (Sufficient Category). In the prompt engineering and hallucination risk variables, the output validation aspect achieved the highest TCR of 78.48%, while the awareness of hallucination risk stood at 73.89%. It can be concluded that the use of LLMs is considered effective in accelerating student learning productivity, yet its application still requires strict critical thinking and independent verification to ensure that the learning outcomes obtained remain valid and accurate. Keywords: Large Language Model; Learning Efficiency; UTAUT; Prompt Engineering; Hallucination Trap;

Pemberdayaan Karang Taruna melalui Teknologi Pengemasan Modern dan Market Desa untuk Penguatan Pemasaran Produk Pertanian di Desa Molingkapoto Selatan Alter Lasarudin; Terri Repi; Widya Kurniati
Jurnal Abdimas Terapan Vol. 5 No. 2 (2026): JURNAL ABDIMAS TERAPAN (MEI)
Publisher : Program Vokasi Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56190/jat.v5i2.130

Desa Molingkapoto Selatan memiliki potensi pertanian yang besar karena mayoritas masyarakat bekerja sebagai petani. Namun, pemasaran produk masih terbatas di tingkat lokal. Permasalahan utama yang dihadapi adalah pengemasan produk yang masih tradisional, belum adanya standar berat dan kebersihan kemasan, lemahnya identitas merek lokal, serta belum optimalnya pemanfaatan pemasaran digital. Kegiatan pengabdian ini bertujuan meningkatkan kapasitas Karang Taruna dalam pengemasan modern dan pemasaran digital produk pertanian desa. Metode kegiatan meliputi observasi, sosialisasi, pelatihan penggunaan alat pengemas modern, pendampingan pembuatan label dan merek produk, pengembangan MarketDesa, serta evaluasi kegiatan secara deskriptif. Hasil kegiatan menunjukkan bahwa Karang Taruna mampu menggunakan alat pengemas modern, menghasilkan kemasan beras berlabel “Beras MolSel”, serta memanfaatkan MarketDesa sebagai kanal pemasaran digital yang terintegrasi dengan WhatsApp dan media sosial. Program ini memperkuat kapasitas pemuda desa, meningkatkan nilai tambah produk pertanian, dan mendukung kemandirian ekonomi berbasis komunitas. Dengan demikian, model transformasi digital berbasis komunitas ini dapat direplikasi pada desa lain yang memiliki potensi produk lokal serupa.

Sistem Pendukung Keputusan Reward Penyidik Terbaik Berbasis Web Menggunakan AHP-SAW Ivone Djafar; Hilmansyah Gani; Alter Lasarudin; Wahyudin Hasyim
Riau Jurnal Teknik Informatika Vol. 5 No. 2 (2026): Juli 2026
Publisher : Prodi Teknik Informatika Universitas Pasir Pengaraian

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30606/rjti.v5i2.4710

The investigator reward process at the Narcotics Investigation Directorate of Gorontalo Regional Police is still conducted manually, making it prone to subjectivity, inconsistency, and time-consuming evaluations. This study aims to develop a web-based Decision Support System (DSS) for selecting the best investigator using the Analytical Hierarchy Process (AHP) and Simple Additive Weighting (SAW) methods. The system was developed using the Rapid Application Development (RAD) approach and evaluated 20 investigator alternatives based on five criteria: Electronic Management of Investigators (EMP), Case Resolution, Discipline, Integrity, and Teamwork. The AHP method was applied to determine the priority weights of the evaluation criteria, while the SAW method was used to calculate preference values and rank the alternatives. The results show that Electronic Management of Investigators (EMP) is the most influential criterion with a weight of 41.62%, while alternative A2 achieved the highest preference value of 0.8923, making it the recommended best investigator. The developed system successfully automates the weighting, ranking, and reporting processes, thereby supporting a more objective, transparent, and measurable decision-making process. The novelty of this study lies in the development of a web-based DSS that integrates administrative and behavioral performance indicators into a single evaluation model for investigator reward selection, providing a more comprehensive assessment than conventional evaluation approaches.

Analisis Pelanggaran Disiplin dan Kode Etik Anggota Polri Menggunakan Decision Tree C4.5 Fahriyadi Purnama Thaib; Frangky Tupamahu; Alter Lasarudin; Hilmansyah Gani
Riau Jurnal Teknik Informatika Vol. 5 No. 2 (2026): Juli 2026
Publisher : Prodi Teknik Informatika Universitas Pasir Pengaraian

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30606/rjti.v5i2.4711

Disciplinary and ethical violations committed by police officers can affect organizational professionalism and reduce public trust in law enforcement institutions. The management of violation records at Polresta Gorontalo Kota remains largely administrative, making it difficult to identify violation patterns and support data-driven decision-making. This study aims to analyze disciplinary and ethical violation data using the Decision Tree C4.5 algorithm to develop a classification model and decision rules. The dataset consists of police disciplinary and ethical violation records collected between 2022 and 2026. The results indicate that the violation category attribute serves as the root node of the decision tree, with the highest Gain Ratio value of 0.694. The resulting model successfully classifies violations into three sanction levels—minor, moderate, and severe—while generating interpretable decision rules. Model evaluation using a confusion matrix achieved an accuracy of 70.8%. The findings demonstrate that the C4.5 algorithm is capable of identifying patterns between violation types and sanction levels, indicating its potential as a decision-support tool for managing disciplinary and ethical violations within the Indonesian National Police.

Usability Sistem Informasi Aparatur Sipil Negara (SIASN) Menggunakan Metode System Usability Scale (SUS) pada BKPSDM Kabupaten Gorontalo Arifin Ahudulu; Alter Lasarudin; Frangky Tupamahu; Hilmansyah Gani
Riau Jurnal Teknik Informatika Vol. 5 No. 2 (2026): Juli 2026
Publisher : Prodi Teknik Informatika Universitas Pasir Pengaraian

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30606/rjti.v5i2.4713

The acceleration of digital transformation in the public sector requires reliable personnel information systems to support efficient and integrated administrative services. The State Civil Apparatus Information System (SIASN) serves as a national platform for managing personnel data and administrative processes of civil servants in Indonesia. However, its implementation at the Regional Civil Service and Human Resource Development Agency (BKPSDM) of Gorontalo Regency still faces several technical and usability-related challenges that may affect user experience and service effectiveness. This study aims to identify user constraints, measure the usability level of SIASN, and provide recommendations for system improvement. A quantitative descriptive approach was employed through data collection using questionnaires, in-depth interviews, and field observations. The study involved 30 active SIASN users, and usability data were analyzed using the System Usability Scale (SUS) instrument. The results indicate that the main obstacles experienced by users include data storage failures, slow system access during peak hours, data updates that have not taken place in real-time, complexity of menu navigation, and limitations of account recovery features. The results of the usability measurement resulted in an average SUS score of 76.4 which is included in the Good category with an Acceptable acceptance rate. These findings show that SIASN is able to support personnel administration activities well and is accepted by users. However, improving the quality of the system is still necessary through optimizing server performance, simplifying the user interface, synchronizing data in real-time, and developing a password reset feature independently. The results of this research are expected to be evaluation materials for developers to improve the quality of digital-based personnel services.

PREDIKSI TINGKAT KEKERASAN PADA PEREMPUAN DAN ANAK DI KABUPATEN GORONTALO MENGGUNAKAN MACHINE LEARNING Citra Ayu; Alter Lasarudin; Wahyudin Hasyim
Jurnal Ilmu Komputer (JUIK) Vol 5, No 3 (2025): OCTOBER 2025
Publisher : Universitas Muhammadiyah Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31314/juik.v5i3.4182

Kekerasan terhadap perempuan dan anak merupakan isu sosial yang masih perlu mendapatkan perhatian khusus, terutama di Kabupaten Gorontalo yang memiliki angka kasus tertinggi di Provinsi Gorontalo. Penelitian ini bertujuan untuk memprediksi tingkat kekerasan terhadap perempuan dan anak menggunakan metode Machine Learning dengan algoritma Naïve Bayes dan Regresi Linier Berganda. Hasil penelitian menunjukkan bahwa Naïve Bayes menghasilkan RMSE sebesar 0.174 untuk kasus perempuan dan 0.254 untuk kasus anak. Sementara itu, Regresi Linier Berganda menghasilkan RMSE yang sangat kecil yaitu 0.000, baik untuk kasus perempuan maupun kasus anak. Dari hasil tersebut, dapat disimpulkan bahwa algoritma Regresi Linier Berganda lebih akurat dalam memprediksi tingkat kekerasan. Dengan hasil penelitian ini, diharapkan dapat membantu pemerintah dan lembaga terkait dalam mengambil langkah pencegahan serta tindakan yang lebih cepat, sehingga kasus kekerasan terhadap perempuan dan anak di Kabupaten Gorontalo dapat dicegah.

PERBANDINGAN ALGORITMA NAIVE BAYES, LOGISTIC REGRESSION DAN ARTIFICIAL NEURAL NETWORK UNTUK KLASIFIKASI TINGKAT KESEJAHTERAAN KELUARGA Natalia R Simon; Alter Lasarudin; Wahyudin Hasyim
Jurnal Ilmu Komputer (JUIK) Vol 5, No 3 (2025): OCTOBER 2025
Publisher : Universitas Muhammadiyah Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31314/juik.v5i3.4931

Desa Batu Hijau adalah salah satu desa di pesisir pantai yang ada diKecamatan Bonepantai Kabupaten Bone Bolango. Saat ini klasifikasi tingkat kesejahteraan keluarga di wilayah tersebut belum sepenuhnya tepat, yang mengakibatkan distribusi subsidi yang tidak tepat sasaran. Oleh karena itu, sangat penting untuk memahami tingkat kesejahteraan keluarga di Desa tersebut. Dalam penelitian ini, penulis menggunakan algoritma Naive Bayes, Logistic Regression, dan Artificial Neural Network. Berdasarkan hasil percobaan menggunakan tool rapidminer dan google colab algoritma naive bayes, logistic regression, dan artificial neural network memiliki nilai akurasi yang sama yaitu 100%, precision 100%, serta recall 100%. Berdasarkan Hasil pengujian menggunakantoolrapidminerdangooglecolabalgoritmaNaiveBayes,LogisticRegression,danArtificialNeural Network mampu mengklasifikasikan data tingkat kesejahteraan keluarga dengan sempurna yang menunjukkan bahwa model mampu mengklasifikasikan data tanpa kesalahan.

PERBANDINGAN METODE MACHINE LEARNING UNTUK PREDIKSI KUNJUNGAN PASIEN PADA RUMAH SAKIT SITTI KHADIDJAH GORONTALO Mohamad Akmal Djeden; Alter Lasarudin; Wahyudin Hasyim
Jurnal Sistem Informasi dan Bisnis Cerdas Vol. 19 No. 1 (2026): Februari 2026
Publisher : Program Studi Sistem Informasi, Fakultas Ilmu Komputer, UPN "Veteran" Jawa Timur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33005/sibc.v19i1.711

The level of patient visits at RSIA Sitti Khadidjah Gorontalo has increased every year as seen from the data from 2020 to 2024. Therefore, researchers will predict the development of patient visits so that they can help RSIA Sitti Khadidjah in providing medical personnel, facilities, and medicines more effectively according to the estimated number of patients. This study aims to apply the Autoregressive Integrated Moving Average (ARIMA) and Neural Network methods in predicting patient visits and to find out the results of predicting patient visits at RSIA Sitti Khadidjah Gorontalo and the results of comparisons using the Autoregressive Integrated Moving Average (ARIMA) and Neural Network methods. The results of this study are the number of data records 60, then the smallest MSE value obtained from the ARIMA algorithm is found in the type of GENERAL Outpatient patient visits with an MSE value of 7.692 and the Neural Network algorithm with the smallest RMSE value is found in PBI Inpatient with an RMSE value of 0.069. Although the evaluation values differ, ARIMA uses MSE while Neural Networks use RMSE. However, considering the smallest MSE and RMSE values, RMSE is superior due to its smaller value. Therefore, the Neural Network algorithm is superior to ARIMA. Keywords: Prediction, Patient Visits, Autoregressive Integrated Moving Average, Neural Network.

Co-Authors Agus Salim Lamusu Antupetu, Rastin Arifin Ahudulu Bagus Setio Raharjo Bakari, Sitti Nurfatimah Citra Ayu Dhian Satria Yudha Kartika Dian Hidayat Rahmola Djainab Humolungo Dwi Fanti Utina Fahriyadi Purnama Thaib Fatiya Ointu Fatmawati Meilan Harun Frangky Tupamahu Gani, Hilmansyah Habie, Khairul Fathan Hadi Pratomo Handayani, Tri Pratiwi Hapsa Husain Hasanah, Ika Nur Hasna Nurain Mukhsin Ibrahim, Irawan Ivone Djafar Larote, Yonis Luawo, Salma Rivani Miranda A. Hasan Misran Tomayahu Mohamad Akmal Djeden Mohamad Ilyas Abas N Dian Hidayat Rahmola Natalia R Simon Noviyanti Patak Lembang Olii, Muhammad Ramdhan Polapa, Risman Purwanto Purwanto Rasmawati Ika Rindiyani Saputri Agu Rizal Lamusu Roy Dumako Roy Dumako Rubiyanto Maku Siti Juliana Pakaya Sitnasanti Yane Stevie Eka Putri Lamato Sukma, Dini Artika SYAHRIAL SYAHRIAL Terri Repi Tri Pratiwi Handayani Wah Ha Wahyudin Hasyim Widya Kurniati Yulanda Yunus Yusuf Lalu

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