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Comparing MCDM Methods for Assessing the Lecturer Performance Index at Dipa Makassar University Suryani, Suryani; Patasik, Madyana; Zeannyfer, Stiffany Lourens; Syahputri, Andhiny Nurakzhany
Brilliance: Research of Artificial Intelligence Vol. 4 No. 2 (2024): Brilliance: Research of Artificial Intelligence, Article Research November 2024
Publisher : Yayasan Cita Cendekiawan Al Khwarizmi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/brilliance.v4i2.4979

Assessing or evaluating the Lecturer's Performance, presented through the Lecturer Performance Index, is a crucial element in the university system that impacts the quality of The University's Three Main Purposes, which include teaching, research, and community service. To ensure objective and accurate evaluations, methods that can accommodate various relevant criteria are needed. One increasingly popular approach is the Multi-Criteria Decision Making (MCDM) method, which allows for evaluating and comparing alternatives based on multiple criteria. This research compares several MCDM methods, namely Weighted Product (WP), Simple Additive Weighting (SAW), and Multi-Objective Optimization by Ratio Analysis (MOORA), used to assess the lecturer's performance at Dipa University Makassar. The WP method can handle criteria with different units, SAW is simpler and easier to apply, while MOORA offers a more comprehensive analysis. This study also identifies challenges in assessing the lecturer's performance, such as the influence of students' subjective evaluations that may lead to bias, as well as the addition of several of the lecturer's performance evaluation criteria such as teaching innovation, student mentoring, international and national journal publications, internal publications, and book publications. Additionally, self-development criteria based on academic fields are considered. The findings of this research are expected to provide insights into effective MCDM methods for the lecturer's performance evaluation and offer recommendations for educational institutions to choose the appropriate and transparent evaluation method. By using MCDM, the objectivity and accuracy of the lecturer's performance evaluations can be improved, biases can be reduced, and contributions can be made toward developing more fair and systematic evaluation standards.

Implementasi Teknologi Aztec Code Pada Desain Sistem Reservasi Tiket Bus Berbasis Android Rismayani, Rismayani; Nurani; Pineng, Martina; Patasik, Madyana; Sambo Layuk, Novita
CSRID (Computer Science Research and Its Development Journal) Vol. 15 No. 1: February 2023
Publisher : LPPM Universitas Potensi Utama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22303/csrid-.15.1.2023.47-61

This research aims to apply the Aztec Code technology to design an Android-based inter-district bus ticket reservation system in South Sulawesi. So far, bus ticket reservations are still made via telephone and even come directly to bus representatives, and the tickets given are still in conventional form. The method used in the implementation uses the Aztec Code, where the Aztec Code will be obtained in digital bus tickets or printed form. Aztec Code is a code that can store alphanumeric information that can be scanned, which makes the Aztec Code easier to read in the scanning process. Furthermore, an Android-based platform is used for smartphone devices in building the system. The result of the research is to design a system that can be used in making bus ticket reservations that are applied to Bintang Prima Makassar, which uses Aztec Code technology and has a good level of ease of reading when scanning the code. Based on the functional testing of the system, valid data is generated.

Penguatan Komunitas Belajar Guru dengan Teknologi Digital: Studi Kasus KKG Malabiri Hanapi, Sitti Harlina; Layuk, Novita Sambo; Patasik, Madyana; Akhriana, Asmah; Samsuddin, Sadly; Usman, Usman; Djafar, Imran; Rizal, Muhammad; Rahmat, Rahmat
Jurnal Pengabdian kepada Masyarakat UBJ Vol. 8 No. 2 (2025): Juni 2025
Publisher : Lembaga Penelitian Pengabdian kepada Masyarakat dan Publikasi Universitas Bhayangkara Jakarta Raya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31599/32hgw568

To improve the digital skills of teachers in KKG Malabiri, a community service program was held, focusing on the use of Google Form and Canva in creating interactive assessment and learning materials. Through a one-day intensive training, 56 elementary school teachers from Minasatene District, Pangkajene Islands Regency, managed to show a significant increase in their understanding, as evidenced by an increase in the average score from 42.1 to 80.9. This program confirms the effectiveness of hands-on training in equipping teachers with relevant digital skills. Through the community service program, teachers in KKG Malabiri received intensive training in the use of Google Form and Canva, which aims to improve their ability to create interactive assessment and learning media. The training, which involved 56 elementary school teachers from Minasatene District, Pangkajene Islands Regency, managed to significantly improve their understanding, with an increase in the average score from 42.1 to 80.9

Comparing MCDM Methods for Assessing the Lecturer Performance Index at Dipa Makassar University Suryani, Suryani; Patasik, Madyana; Zeannyfer, Stiffany Lourens; Syahputri, Andhiny Nurakzhany
Brilliance: Research of Artificial Intelligence Vol. 4 No. 2 (2024): Brilliance: Research of Artificial Intelligence, Article Research November 2024
Publisher : Yayasan Cita Cendekiawan Al Khwarizmi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/brilliance.v4i2.4979

Assessing or evaluating the Lecturer's Performance, presented through the Lecturer Performance Index, is a crucial element in the university system that impacts the quality of The University's Three Main Purposes, which include teaching, research, and community service. To ensure objective and accurate evaluations, methods that can accommodate various relevant criteria are needed. One increasingly popular approach is the Multi-Criteria Decision Making (MCDM) method, which allows for evaluating and comparing alternatives based on multiple criteria. This research compares several MCDM methods, namely Weighted Product (WP), Simple Additive Weighting (SAW), and Multi-Objective Optimization by Ratio Analysis (MOORA), used to assess the lecturer's performance at Dipa University Makassar. The WP method can handle criteria with different units, SAW is simpler and easier to apply, while MOORA offers a more comprehensive analysis. This study also identifies challenges in assessing the lecturer's performance, such as the influence of students' subjective evaluations that may lead to bias, as well as the addition of several of the lecturer's performance evaluation criteria such as teaching innovation, student mentoring, international and national journal publications, internal publications, and book publications. Additionally, self-development criteria based on academic fields are considered. The findings of this research are expected to provide insights into effective MCDM methods for the lecturer's performance evaluation and offer recommendations for educational institutions to choose the appropriate and transparent evaluation method. By using MCDM, the objectivity and accuracy of the lecturer's performance evaluations can be improved, biases can be reduced, and contributions can be made toward developing more fair and systematic evaluation standards.

Penguatan keterampilan teknologi informasi bagi guru melalui pelatihan Microsoft Office di UPTD SDN 168 Inpres Jambua Ahyuna, Ahyuna; Akhriana, Asmah; Patasik, Madyana; Magfirah, Magfirah; Khaddafi, Muh.; Layuk, Novita Sambo; Aini, Nurul; Santi, Santi; Aisa, Sitti; Piu, Sri Wahyuningsi; Arifin, Suci Ramadhani
KACANEGARA Jurnal Pengabdian pada Masyarakat Vol 7, No 4 (2024): November
Publisher : Institut Teknologi Dirgantara Adisutjipto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28989/kacanegara.v7i4.2240

Kegiatan pengabdian kepada masyarakat ini bertujuan untuk meningkatkan profesionalisme guru di UPTD SDN 168 Inpres Jambua melalui pelatihan Microsoft Office. Pelatihan yang diikuti oleh 20 orang guru ini berfokus pada pemanfaatan aplikasi Word, Excel, dan PowerPoint untuk pembelajaran yang efektif dan inovatif. Metode yang digunakan meliputi pelatihan teori dan praktik, pendampingan, serta evaluasi pemahaman peserta. Hasilnya, keterampilan guru dalam memanfaatkan teknologi informasi untuk pembelajaran berkualitas meningkat. Evaluasi kuesioner menunjukkan respon positif dari peserta terkait materi, penyampaian, dan kebermanfaatan pelatihan. Mayoritas menyatakan pelatihan sesuai kebutuhan, memberikan keterampilan baru, dan bermanfaat bagi pembelajaran. Sebagian besar peserta menyatakan akan menerapkan keterampilan tersebut. Kegiatan ini juga menjalin sinergi antara perguruan tinggi dan sekolah dalam meningkatkan mutu pendidikan. Peserta terbaik berpotensi menjadi agen perubahan dalam penerapan teknologi informasi di sekolah. Tantangan yang dihadapi adalah durasi pelatihan yang dirasa kurang dan perlunya pendampingan lebih lanjut. Secara keseluruhan, kegiatan ini memberikan kontribusi positif dalam pengembangan profesionalisme guru melalui penguasaan Microsoft Office.

Implementasi Algoritma Support Vector Machine (SVM) pada Pengklasifikasian Sentimen Warganet terhadap Juru Parkir Liar Patasik, Madyana; S, Santi; M, Muhardi; R, Thabrani; T, Husain
Buletin Sistem Informasi dan Teknologi Islam (BUSITI) Vol 6, No 3 (2025)
Publisher : Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/busiti.v6i3.3080

Juru parkir liar dapat dengan mudah ditemukan di Kota Makassar dan keberadaannya ini sering meresahkan warga. Oleh karena itu, penelitian ini bertujuan untuk mengklasifikasikan sentimen negatif warganet terhadap juru parkir liar tersebut. Dengan menggunakan algoritma Support Vector Machine (SVM), dari 200 data yang dikumpulkan melalui kuesioner daring, 80% (160 responden) digunakan untuk data latih dan 20% (40 responden) untuk data uji. Hasil menunjukkan bahwa model SVM berhasil mengklasifikasikan sentimen, negatif (70% atau 28 responden) dan tidak negatif (30% atau 12 responden) dari 40 data uji dengan tingkat akurasi sebesar 95%, precision 1.00, recall 1.00, dan F1-score 1.00 untuk kelas/label “positif” (sentimen negatif), precision 0.83, recall 0.83, dan F1-score 0.91 untuk kelas/label “negatif” (sentimen tidak negatif). Dengan demikian, dapat disimpulkan bahwa penelitian ini membuktikan efektivitas algoritma SVM dalam mengklasifikasikan sentimen terhadap juru parkir liar. Hasil yang diperoleh dapat menjadi bahan pertimbangan pihak berwenang dalam menertibkan kota, terutama area sekitar pertokoan atau pusat perbelanjaan.

Penerapan Algoritma K-Nearest Neighbor (KNN) Dalam Pengklasifikasian Tingkat Kematangan Buah Nangka Berdasarkan Citra Warna Kulit Santi, Santi; Susanto, Cucut; Muhardi, Muhardi; Patasik, Madyana; Nurlina, Nurlina
Digital Transformation Technology Vol. 4 No. 1 (2024): Periode Maret 2024
Publisher : Information Technology and Science(ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/digitech.v4i1.4550

Nangka (Artocarpus heterophyllus Lamk) adalah buah tanaman tropis yang berasal dari India yang memiliki aroma khas dan tajam serta daging buah berwarna kuning segar dengan rasa yang manis. Buah ini bisa dikonsumsi langsung sama seperti buah pada umumnya, bisa juga diolah menjadi masakan seperti sayur, cemilan atau menjadi bahan campuran dessert. Namun faktanya, masih banyak orang yang belum bisa membedakan antara buah nangka yang masih mengkal, matang dan yang sudah sangat matang dari warna kulit buah dengan beberapa faktor yang dapat mempengaruhi seperti karena usia, buta warna, dan lain-lain. Oleh karena itu, tujuan dari penelitian ini adalah untuk merancang aplikasi pengklasifikasian tingkat kematangan buah nangka yang berbasis android. Untuk mencapai tujuan ini, metode Hue, Saturation, Value (HSV) diterapkan dalam pengekstraksian warna kulit buah dan algoritma K-Nearest Neighbor (KNN) untuk mengklasifikasikan tingkat kematangan buah nangka tersebut. Penelitian ini dimulai dengan studi literatur, desain sistem/aplikasi, pengumpulan data, menganalisis dan mengolah data. Aplikasi dirancang menggunakan bahasa pemrograman flutter dan Python. Sebanyak 99 citra buah nangka (33 citra per kategori) sebagai data training dan sebanyak 30 citra buah nangka sebagai data testing dan hasil klasifikasi menunjukkan tingkat akurasi sebesar 78%. Kesimpulannya adalah bahwa perpaduan antara algoritma KNN dengan metode HSV dapat diandalkan dalam pengklasfikasian tingkat kematangan buah.

Perancangan Aplikasi Koperasi Simpan Pinjam pada KSP Harapan Baru Sejahtera Berbasis Web dengan Metode Algoritma K-Nearest Neighbor Nasriani, Nasriani; Ibrahim, M.; Herlinda, Herlinda; Patasik, Madyana
DIPAKOMSI Vol. 17 No. 2 (2023): Jurnal Dipanegara Komputer Sistem Informasi (DIPAKOMSI)
Publisher : Pusat Penelitian dan Pengabdian kepada Masyarakat Universitas Dipa Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36774/dipakomsi.v17i2.1516

Layanan jasa simpan pinjam pada KSP Harapan Baru Sejahtera pada saat ini masih diproses dengan menggunakan alat seperti buku untuk mencatat laporan dari nasabah dan kalkulator untuk menghitung penilaian. Dari permasalahan-permasalahan tersebut maka membutuhkan adanya pengolahan data koperasi yang terkomputerisasi untuk memudahkan pengolahan data koperasi. diusulkan yaitu Sistem Informasi Koperasi Simpan Pinjam Pada KSP Harapan Baru Sejahtera Berbasis Web menggunakan metode KNN. KSP Harapan Baru Sejahtera dapat menjadi lebih mudah dalam proses identifikasi laporan pemeriksaaan dan penilaian dari nasabah berdasarkan kriteria yang digunakan menggunakan kriteria yang ditentukan yaitu point usaha, point pinjaman, point resiko, point pekerjaan dan point simpanan yang akan digunakan sebagai tahapan dari K-Nearest Neighbor yaitu pengumpulan data sampel penilaian, normalisasi data, proses hitung jarak Euclidean lalu hasil perangkingan. Hasil pengujian diatas dengan menggunakan 9 skenario pengujian maka dapat disimpulkan bahwa web dapat berjalan sesuai dengan fungsionalitas dan sesuai dengan yang diharapkan.

Learning Difficulty Levels Prediction of Elementary School Student Mathematics Using Machine Learning Model Rismayani, Rismayani; Sambo Layuk, Novita; Patasik, Madyana; Endang, Andi Hutami
Journal of Information Technology and Its Utilization Vol 8 No 1 (2025): June 2025
Publisher : Sekolah Tinggi Multi Media

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56873/jitu.8.1.5906

Difficulty learning mathematics in elementary school students is a significant problem and requires serious attention. This study aims to predict the difficulty level in elementary school students learning mathematics using a machine learning model, namely KNN. Exam scores, assignments, quizzes, and characteristics of students' difficulty level in learning mathematics were used as data in this study. A study used the KNN model to divide students into three categories of difficulty in learning mathematics: easy, moderate, and challenging. The results showed that the KNN model can accurately predict student’s difficulty levels in mathematics. Thus, applying this model can help teachers provide appropriate and effective interventions to students experiencing difficulties. Using machine learning technology, especially the KNN model, we found an accuracy of 95%. In addition, we can still accurately predict the difficulty level of elementary school students' mathematics learning. This study uses anonymous student data, the distribution of assignments, quizzes, and exam score ranges, and characteristics of mathematics learning difficulty levels. There are three prediction classes: high, medium, and low.

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