Determining eligible villages for mobile services using k-NN algorithm

To maximize and get population document services closer to the community, the Disdukcapil district of Alor provides mobile services by visiting people in remote villages which difficult-to-reach service centres in the city. Due to a large number of villages and limited time and costs, not all villages can be served, so the kNN algorithm is needed to determine which villages are eligible to be served. The criteria used in this determination are village distance, difficulty level, and document ownership (Birth Certificate, KIA, family card, and KTPel). The classes that will be determined are "Very eligible", "Eligible", and "Not eligible". By applying Z-Score normalization with the value of K=5, the classification gets 94.12% accuracy, while non-normalized only gets 88.24% accuracy. Thus, applying normalization to training data can improve the kNN algorithm's accuracy in determining eligible villages for "ball pick-up" or mobile services

Z Distance Function for KNN Classification

This paper proposes a new distance metric function, called Z distance, for KNN classification. The Z distance function is not a geometric direct-line distance between two data points. It gives a consideration to the class attribute of a training dataset when measuring the affinity between data points. Concretely speaking, the Z distance of two data points includes their class center distance and real distance. And its shape looks like "Z". In this way, the affinity of two data points in the same class is always stronger than that in different classes. Or, the intraclass data points are always closer than those interclass data points. We evaluated the Z distance with experiments, and demonstrated that the proposed distance function achieved better performance in KNN classification

Analisis K-Means dan Self Organizing Maps pada data relevansi program studi dan pekerjaan lulusan S1 Informatika

ABSTRAK Perguruan tinggi merupakah salah satu tingkatan dalam menuntut ilmu yang selalu diharapkan dapat menciptakan lulusan yang mampu serta kompeten dengan bidang ilmunya sehingga diharapkan dapat terserap di dunia kerja sesuai dengan apa yang dipelajari. Maraknya fenomena para lulusan S1 yang bekerja tidak sesuai dengan jurusan yang dipelajari menjadikan perlunya sebuah evaluasi dalam perguruan tinggi. Untuk dapat melakukan evaluasi tersebut, maka perlu dilakukan pengukuran akan relevansi pekerjaan para lulusan S1 dengan apa yang mereka pelajari sesuai dengan capaian pembelajaran serta pengelompokan hasil pengukuran tersebut menggunakan teknik data mining dengan metode Clustering. K-Means dan Self Organizing Maps digunakan dalam RStudio untuk melihat seperti apa hasil pengelompokan data tersebut. Hasil analisa menunjukkan bahwa sebesar 53% lulusan mendapatkan pekerjaan yang sesuai dengan bidang teknik informatika, 29% lulusan kurang memenuhi capaian pembelajaran namun mendapat pekerjaan yang sesuai dan 18% lulusan tidak mendapatkan pekerjaan yang sesuai dengan bidang informatika. مستخلص البحث التعليم العالي هو أحد مستويات الدراسة التي من المتوقع دائما أن تنتج خريجين قادرين وأكفاء في مجال علمهم بحيث يتوقع منهم استيعابهم في عالم العمل وفقا لما درسوه. إن تزايد ظاهرة خريجي المرحلة الجامعية الذين يعملون بطرق لا تتناسب مع التخصص الذي يدرسونه يجعل الحاجة إلى التقييم في التعليم العالي. لكي نتمكن من إجراء هذا التقييم، من الضروري قياس مدى صلة عمل خريجي المرحلة الجامعية بما تعلموه وفقًا لنتائج التعلم وتجميع نتائج القياس باستخدام تقنيات التنقيب عن البيانات مع طريقة التجميع. يتم استخدام وسائل K وخرائط التنظيم الذاتي لمعرفة كيف تبدو نتائج تجميع البيانات. تظهر نتائج التحليل أن 53% من الخريجين حصلوا على وظائف تناسب مجال الهندسة المعلوماتية، و29% من الخريجين لم يحققوا مخرجاتهم التعليمية ولكنهم حصلوا على وظائف مناسبة و18% من الخريجين لم يحصلوا على وظائف تناسب المجال المعلوماتية. ABSTRACT Higher education is one of the levels of studying that is always expected to produce graduates who are capable and competent in their field of knowledge so that they are expected to be absorbed in the world of work according to what they have studied. The increasing phenomenon of undergraduate graduates who work in ways that do not match the major they are studying makes the need for an evaluation in higher education. To be able to carry out this evaluation, it is necessary to measure the relevance of the work of undergraduate graduates to what they have learned according to learning outcomes and group the measurement results using data mining techniques with the Clustering method. K-Means and Self Organizing Maps are used in RStudio to see what the results of grouping the data look like. The results of the analysis show that 53% of graduates got jobs that fit the field of informatics engineering, 29% of graduates did not meet their learning outcomes but got jobs that were suitable and 18% of graduates did not get jobs that fit the field of informatics